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Abstract Changing climatic conditions have escalated the risk of compound disaster, and there remains a scarcity of quantitative research at river basin scale. An integrated research framework is proposed in this study to quantitatively analyze and assess the risk of future compound flood in Xijiang River Basin based on external driving factor and internal variables. Under this framework, a multi-model ensemble of 10 preferred CMIP6 GCMs is carried out based on statistical downscaling and Bayesian weighted average method, and the multi-scale variation characteristics of precipitation and runoff during 2020 ~ 2099 are analyzed based on the ensemble data. Combined with univariate and multivariate trend analysis considering intra-correlation, the multi-class copula functions are utilized to estimate the joint probability and return period of compound flood. The results show that: 1) The precipitation and runoff increase by 8.25%, 14.5%, and 34.05%, 55.18% respectively compare to the baseline period under SSP2-4.5 and SSP5-8.5, with both displaying an increasing trend at rates of 1.03%/10a, 2.66%/10a, and 2.74%/10a, 4.62%/10a on the interdecadal scale under the two scenarios, respectively. 2) The internal variables of the compound flood represented by the annual maximum peak flow (AMPF) and the annual consecutive maximum 7-day flood volume (AM7dFV) present a significant increasing trend under the two scenarios, but the annual maximum precipitation (AMPre) of the external driving factor does not show a significant trend while the annual total precipitation (ATPre) of the external driving factor increases significantly under both scenarios. It is noteworthy that both the internal variables and the external driving factors of compound flood show significant increases in the multivariate analysis. 3) The joint variable of compound flood demonstrates a substantially increasing trend under both scenarios, along with an increase in the magnitude of the once-in-a-century flood. Discounting the intra-correlation between multivariate, the degree of disaster would be underestimated.
Zhao et al. (Wed,) studied this question.