The efficient operation of multi-reservoirs is highly beneficial for securing supply for prevailing demand and ecological flow. This study proposes a monthly hedging rule-based aggregation–decomposition model for optimizing a parallel reservoir system. The proposed model, which is an aggregated hedging rule for ecological flow (AHRE), uses external optimization to determine the total release of the reservoir system based on improved hedging rules—the optimization model aims to minimize water demand and ecological flow deficits. Additionally, inner optimization distributes the release to individual reservoirs to maintain equal reservoir storage rates. To verify the effectiveness of the AHRE, a standard operation policy and transformed hedging rules were selected for comparison. Three parallel reservoirs in the Naesung Stream Basin in South Korea were selected as a study area. The results of this study demonstrate that the AHRE is better than the other two methods in terms of supplying water in line with demand and ecological flow. In addition, the AHRE showed relatively stable operation results with small water-level fluctuations, owing to the application of improved hedging rules and a decomposition method. The results indicate that the AHRE has the capacity to improve downstream river ecosystems while maintaining human water use and provide a superior response to uncertain droughts.

As urbanization intensifies, the ecological area ratio (EAR) has become an essential tool for assessing ecological performance in urban development projects. However, conventional EAR systems remain largely prescriptive and surface-oriented, lacking adaptability to diverse planning contexts. This study proposes a probability-based EAR reference table developed from empirical data collected across six representative large-scale urban development districts. EAR values were statistically analyzed for 16 land-use types to construct a reference table using mean and quartile indicators. The table was then applied to seven newly planned towns to evaluate its predictive utility. The results showed that predicted EAR values closely aligned with institutional targets and revealed meaningful internal variation depending on land-use composition. Green space and parks showed the highest ecological contributions, while multi-family housing, despite moderate unit-area performance, played a key stabilizing role due to its large spatial footprint. Correlation analyses further demonstrated that EAR composition varied across housing supply types, shaped by differing regulatory frameworks and design priorities. By transitioning EAR from a uniform ratio to a data-driven, probabilistic guideline, this study offers both a practical estimation tool and a strategic planning aid. The findings provide actionable insights for more adaptive, equitable, and ecologically robust urban development practices.