Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil

被引:7
作者
Anzolin, Gabriel [1 ]
Chaffe, Pedro Luiz Borges [2 ,4 ]
Vrugt, Jasper A. [3 ]
AghaKouchak, Amir [3 ]
机构
[1] Univ Fed Santa Catarina, Grad Program Environm Engn, Florianopolis, Brazil
[2] Univ Fed Santa Catarina, Dept Sanitary & Environm Engn, Florianopolis, Brazil
[3] Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA USA
[4] Fed Univ Santa Catarina UFSC, Technol Ctr CTC, Dept Sanitary & Environm Engn, Delfino Conti St Trindade,CxP 476, BR-88040970 Florianopolis, Brazil
关键词
floods; frequency analysis; nonstationarity; climate information; STATIONARITY; PRECIPITATION; EVAPORATION; SERIES; DEAD;
D O I
10.1080/02626667.2023.2182212
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Climatic drivers of floods have been widely used to improve nonstationary flood frequency analysis (FFA). However, the forecast ability of nonstationary FFA with out-of-sample prediction has not been comprehensively evaluated. We use 379 flood records from Brazil to assess the ability of process-informed nonstationary models for out-of-sample FFA using the generalized extreme value (GEV) distribution. Five drivers of floods are used as covariates: annual temperature, El Nino Southern Oscillation, annual rainfall, annual maximum rainfall, and annual maximum soil moisture content. Our results reveal that a nonstationary model is preferable when there is a significant correlation between flood and climate covariates in both the training period and full record. The rainfall-based covariates lead to better out-of-sample nonstationary FFA models. These findings highlight that using climate information as covariates in nonstationary FFA is a promising approach for estimating future floods and, hence, better infrastructure design, risk assessment and disaster preparedness.
引用
收藏
页码:645 / 654
页数:10
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