  Predicting future probable values of model parameters, is an essential
pre-requisite for assessing model decision reliability in an uncertain
environment. Scenario Analysis is a methodology for modelling uncertainty in
water resources management modelling. Uncertainty if not considered
appropriately in decision making will decrease reliability of decisions,
especially in long-term planning. One of the challenges in Scenario Analysis is
how scenarios are made. One of the most approved methods is statistical
modelling based on Auto-Regressive models. Stream flow future scenarios in
developed basins that human has made changes to the natural flow process could
not be generated directly by ARMA modelling. In this case, making scenarios for
monthly rainfall and using it in a water resources system model makes more
sense. Rainfall is an ephemeral process which has zero values in some months
which introduces some limitations in making use of monthly ARMA model.
Therefore, a two stage modelling approach is adopted here which in the first
stage yearly modelling is done. Within this yearly model three ranges are
identified: Dry, Normal and Wet. In the normal range yearly ARMA modelling is
used. Dry and Wet range are considered as random processes and are modeled by
frequency analysis. Monthly distribution of rainfall, which is extracted from
available data from a moving average are considered to be deterministic and
fixed in time. Each rainfall scenario is composed of a yearly ARMA process
super-imposed by dry and wet events according to the frequency analysis. This
modelling framework is applied to available data from three rain-gauge stations
in Iran. Results show this modelling approach has better consistency with
observed data in comparison with making use of ARMA modelling alone.
