A Bayesian-model-averaging Copula (i.e., BMAC) approach was proposed for correlation analysis of monthly rainfall and runoff in Xiangxi River watershed, China. The BMAC approach was formulated by incorporating existing Bayesian model averaging (i.e., BMA) method and Archimedean Copula techniques (e.g., Gumbel-Hougaard, Clayton and Frank Copulas) within a general bivariate hydrologic correlation analysis framework. In this paper, the BMA method was applied to determine the marginal distribution functions of variables, and the Copula method was used to analyze the correlation. Results showed that: 1) the BMA method could improve the representation of the marginal distribution of hydrological variables with smaller corresponding errors; 2) the predictive joint distributions of monthly rainfall and runoff was much better calibrated by the Gumbel Copula according to criteria of the root mean square error (i.e., RMSE), Akaike Information Criterion (i.e., AIC) values, Anderson-Darling test (i.e., AD test), and Cramer-von Mises test (i.e., CM test); and 3) the bivariate joint probability and return periods of rainfall and runoff based on the optimal Copula function was characterized and the monthly rainfall and runoff presented a strong positive correlation based on Kendall and Spearman’s rank correlation coefficients. Therefore, the BMAC approach performed reasonably well and can be further used to simulate runoff values according to the historical and predicted rainfall data. Highlights: 1) A Bayesian-model-averaging Copula method is proposed for correlation analysis; 2) the monthly rainfall and runoff in Xiangxi River watershed has a positive correlation. 3) Gumbel Copula is the best in modelling the joint distributions in the Xiangxi River watershed.

Accurate runoff simulation and prediction are crucial for water resources management, especially under the impact of climate change. In this study, a multi-physics ensemble Bayesian model averaging (MPE-BMA) model is developed to improve runoff prediction accuracy by integrating a soil and water assessment tool (SWAT), hydrologiska byråns vattenbalansavdelning (HBV) model, and Bayesian model averaging (BMA) into a general framework. The MPE-BMA model integrates the strengths of the SWAT and HBV models. This approach enhances the robustness of simulation outputs and reduces uncertainties from single-model methods. MPE-BMA is subsequently employed to simulate and predict runoff for the upper reaches of Xiangxi River Basin (XXRB) in China, where four general circulation models (GCMs) and three shared socioeconomic pathways (SSP126, SSP245, and SSP585) are considered. Multiple statistical metrics (R², NSE, and RMSE) prove that the MPE-BMA model outperforms the single models of SWAT and HBV. Results reveal that higher-emission scenarios generally lead to significant decreases in runoff, particularly by the 2080s. Specifically, under SSP585, runoff is projected to decrease by approximately 4.61–12.68% by the 2040s and 5.96–11.28% by the 2080s compared to the historical period. From the perspective of monthly and seasonal runoff changes, the peak runoff is projected to shift from June to May by the 2080s. Additionally, under SSP585, spring and summer runoffs tend to significantly increase, while winter runoff decreases sharply, leading to wetter summers and drier winters. These findings underscore the importance of enhancing water use efficiency, upgrading hydropower stations, and implementing watershed management practices to ensure sustainable water resources management in the XXRB amidst climate change.

ZnO nanorod nonwoven fabrics (ZNRN) were developed through hydrothermal synthesis to facilitate the prevention of the transmission of respiratory pathogens. The superhydrophobicity and antibacterial properties of ZNRN were improved through the response surface methodology. The synthesized material exhibited significant water repellency, indicated by a water contact angle of 163.9°, and thus demonstrated antibacterial rates of 91.8% for Escherichia coli (E. coli) and 79.75% for Staphylococcus aureus (S. aureus). This indicated that E. coli with thinner peptidoglycan may be more easily killed than S. aureus. This study identified significant effects of synthesis conditions on the antibacterial effectiveness, with comprehensive multivariate analyses elucidating the underlying correlations. In addition, the ZnO nanorod structure of ZNRN was characterized through SEM and XRD analyses. It endows the properties of superhydrophobicity (thus preventing bacteria from adhering to the ZNRN surface) and antibacterial capacity (thus damaging cells through the puncturing of these nanorods). Consequently, the alignment of two such features is desired to help support the development of personal protective equipment, which assists in avoiding the spread of respiratory infections.