For cascade reservoirs in the period after main flood process

decision-making on the timing

rate and sequence of their refilling is faced with problems such as runoff forecast uncertainty and complex multi-objective competition-cooperation relationships. To deal with such difficulties in the refilling decision-making

we develop a stochastic optimization operation model for the water refilling of cascade reservoirs with five objectives

power generation

refilling degree

ecology

and upstream and downstream flood risks. We generate a non-inferior solution set for the refiling

use the K-means clustering method to extract its features

and analyze the contradiction relationship between the objectives and the refilling mechanism. Application to a case study of the cascade reservoirs on the Yangtze River mainstream from Xiluodu to Gezhouba shows a drier water refilling period leads to a greater difficulty in the refilling and lower comprehensive benefits. For these reservoirs

the total power output in a dry year is 21.6% lower than that of a wet year; the strongest contradiction occurs between upstream flood control safety and power generation

with the correlation coefficient decreasing by 0.047 from a dry to wet year. The refilling in the Xiluodu and Three Gorges reservoirs varies in stages

rapid in the early stage and slow in the middle and later stages. The multi-objective stochastic programming and clustering analysis method developed in this study for reservoir refilling helps formulate better refilling schemes.