The objective of this study was to capture the dependence between the lake water level and hydro-meteorological variables, and explore the possibility of using a vine copula model for predicting the long-term water level. To achieve this objective, a multidimensional variable vine copula model was constructed and the monthly and daily water levels were predicted from different combinations of the hydro-meteorological factors. The accuracy of the predictions from the constructed vine copula model was tested by comparing the simulation results with those from a BP neural network model and a support vector regression (SVR) model. A vine copula model for lake management was established to predict the lake water level for small data sets. The accuracy of the model was tested and improved by error analysis, such that the obtained lake water level values were further refined.