Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 10⁸ m³. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 10⁸ m³. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system.

Hydrological climate-impact projections in the future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate change-impacted assessment, in a representative watershed of Northeastern China. Moreover, recent studies have indicated that the climate internal variability (CIV) plays an important role in various hydrological climate-impact projections. Six downscaled global climate models (GCMs) under two emission scenarios, and a calibrated Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need to be particularly partitioned and quantified. Moreover, it is worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and the sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominant contributors of runoff in the rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to the runoff in January to May, and October to December. The findings of this study advised that the uncertainty is complex in the hydrological simulation process; hence, it is meaningful and necessary to estimate the uncertainty in the climate simulation process. The uncertainty analysis results can effectively provide efforts for reducing uncertainty, and then give some positive suggestions to stakeholders for adaption countermeasures under climate change.