ABSTRACT
ABSTRACT: An a priori probability distribution for a Tailings Management Facility (TMF) water balance variable (pond volume) is one in which you can see that it is true just lying on your couch. You don’t have to get up off your couch and go outside and examine the way things are in the physical world. You don’t have to do any science. In a posteriori probability distribution for a TMF water balance variable, knowledge or justification is dependent on water balance outcomes or empirical evidence. This paper will examine the difference between modern water balance models where the selected input parameters (precipitation and evaporation) are assigned a priori probability functions and a large number of water balance output realizations for a given variable (pond volume) are generated, versus examining all possible combinations of input parameters and operational parameters (TMF evolution) and then fitting a posteriori probability function to the outcomes.
