  Long-term reservoir management often uses bounds on the reservoir level,
between which the operator can work. However, these bounds are not always kept
up-to-date with the latest knowledge about the reservoir drainage area, and
thus become obsolete. The main difficulty with bounds computation is to
correctly take into account the high uncertainty about the inflows to the
reservoir. In this article, we propose a methodology to derive minimum bounds
while providing formal guarantees about the quality of the obtained solutions.
The uncertainty is embedded using either stochastic or robust programming in a
model-predictive-control framework. We compare the two paradigms to the
existing solution for a case study and find that the obtained solutions vary
substantially. By combining the stochastic and the robust approaches, we also
assign a confidence level to the solutions obtained by stochastic programming.
The proposed methodology is found to be both efficient and easy to implement.
It relies on sound mathematical principles, ensuring that a global optimum is
reached in all cases.
