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.

The Guidelines for Sustainable Aquaculture (GSAs) emphasize that formulating proper regional development plans is essential for achieving sustainable growth in aquaculture. Conventional research has limitations in allocating resources from a regional scale, subject to various constraints relating to the social, economic, environmental, and other domains. To fill this research gap, this study proposes an inexact regional aquaculture system planning model (IRAM) to optimally allocate resources including energy, water, area, media, and labor among various aquaculture species across multiple aquafarms under uncertainty. The model is applied to Fujian Province, China, over a 15-year planning horizon (2026–2040, three 5-year periods). As model-based scenario insights derived under specific assumptions, the main findings are as follows: (i) the system cost would increase by [18.75, 20.00]%, while the product output would greatly rise by [41.57, 47.54]% from period 1 to period 3; (ii) renewable energy (wind and solar) would supply [60.32, 81.05]% of the total electricity by period 3; (iii) shallow-sea culture ([48.6, 50.2]%) and pond culture ([17.6, 18.8]%) would receive the highest media allocation via internet platforms, supporting community communication; and (iv) pollutant and carbon emissions would decline due to the adoption of low-carbon feed and renewable energy penetration. The findings are scenario-dependent, and their practical applicability relies on the sensitivity analysis and model code/data availability discussed in the main text. One limitation lies in its inadequate ability to handle stochastic and fuzzy information commonly encountered in real-world planning problems.