Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a nonstationary model through the Bayesian weighted averaging of two lumped conceptual rainfall–runoff (RR) models (the Xinanjiang and GR4J model) with time-varying weights; and (ii) detecting the temporal variation in the optimized Bayesian weights under climate change conditions. By combining the BMA method with period partition and time sliding windows, the efficacy of adopting time-varying model structures is investigated over three basins located in the U.S. and Australia. The results show that (i) the nonstationary ensemble-averaged model with time-varying weights surpasses both individual models and the ensemble-averaged model with time-invariant weights, improving $NSE[\sqrt{Q}]$ from 0.04 to 0.15; (ii) the optimized weights of Xinanjiang model increase and that of GR4J declines with larger precipitation, and vice versa; (iii) the change in the optimized weights is proportional to that of precipitation under monotonic climate change, as otherwise the mechanism changes significantly. Overall, it is recommended to adopt nonstationary structures in hydrological modeling.

Vegetation within riparian and floodplain undergoes significant alterations driven by climatic factors and human interventions, particularly influenced by cascade hydropower development. This study investigated the dynamics of the Normalized Difference Vegetation Index (NDVI) in riparian and representative floodplains vegetation under cascade hydropower development in the middle reach of Hanjiang River by using Landsat imagery and hydroclimatic station data. The vegetation NDVI of the riparian increased significantly (p < 0.01) during the growing season, and the vegetation NDVI of the riparian and typical floodplains also increased after the hydropower developments. In terms of the key driving factors, the increased annual water level may explain the reduction in most floodplains. Increasing temperature, especially in March, can promote vegetation growth of riparian and typical floodplains. The development of cascade hydropower may result in different contributions of climate and hydrology to vegetation at different periods, and it was found that the climate is the major contributor to the changes in the vegetation NDVI after the construction of the dam. This research will help clarify the impact of cascade hydropower development on vegetation in riparian and floodplain ecosystems. It also provides a scientific basis for vegetation protection and environmental restoration in the basin.