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A copula-based analysis of projected climate changes to bivariate flood quantiles

Yin, Jiabo, Guo, Shenglian, He, Shaokun, Guo, Jiali, Hong, Xingjun, Liu, Zhangjun
Journal of hydrology 2018 v.566 pp. 23-42
climate change, climate models, floods, hydrologic models, isogenic lines, probability, risk assessment, simulation models, stream flow, watersheds, China
Climate change will lead to great impacts on flood frequency curve and design floods in the future. However, traditional hydrologic approaches often fail to analyze the flood characteristics within a bivariate framework under changing environment. Moreover, previous studies investigating bivariate characteristics of flood usually do not derive the adaptive flood quantiles. This study assesses the implications of climate change for future bivariate quantiles of flood peak and volume in Ganjiang River basin, China. The outputs of two global climate models (BNU-ESM and BCC-CSM1.1) are statistically downscaled by Daily bias correction (DBC) method and used as inputs of the Xinanjiang hydrological model to simulate streamflow during 1966–2099. Projections for future flood (2020–2099) under Representative Concentration Pathway (RCP) 8.5 scenario are divided into two 40-year horizons (2040s, 2080s) and a comparison is made between these time horizons and the baseline (1966–2005). Univariate flood frequency analysis indicates that there is a considerable increase in the magnitude and frequency of flood under the RCP8.5 scenario, especially for the higher return periods. The bivariate quantile curves under different levels of Joint Return Period (JRP) for historical and future periods are derived by copula functions and the most likely realizations are estimated. It is found that climate change has heavier impacts on the future joint bivariate quantiles for larger return periods. Finally the adaptive isolines and most likely flood quantiles under a JRP are derived from analyzing the merged series by non-stationary copula-based models. The results highlight that the joint probability, illustrated by the isoline of a given JRP, varies significantly over time when non-stationary models are applied. This study incorporates the impacts of climate change on bivariate flood quantiles and develops an adaptive quantile estimation approach, which may provide useful information for the references of flood risk assessment and management under changing environment.