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

ABSTRACT

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.

KEYWORDS:

• floods
• frequency analysis
• nonstationarity
• climate information

Editor A. Castellarin; Guest Editor E. Volpi

Editor A. Castellarin; Guest Editor E. Volpi

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02626667.2023.2182212

Additional information

Funding

This work was supported by the CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).