The primary aim of this paper is to demonstrate the value of additional information for the efficiency of water projects. The theoretical approach is verified along with the planning process of a hydropower project and a flood-protection project in Bolivia. In these cases the available information consists in observed hydrological data of a river in the high mountains, resp. Prior information is obtained from river gauges in a larger region around the project area. The planning purpose is the optimum design of a hydropower plant.
Bayesian theory is applied in order to minimize the risk of such over- or underdesign.
In this context we have to distinguish between two types of hydrological uncertainty. The natural uncertainty is due to the random variation of hydrometeorological processes, which is unavoidable, i.e. it exists always, even if all possible information on that process were known. In this context runoff, for example, in a river has to be considered as a random variable. Its random variability is due not only to the stochasticity of the causative rainfall process but also due to interception, infiltration, snow melt, evapotranspiration, etc. The information uncertainty is due to the effect that data observed from nature represent always only samples of a unknown population.