MODELING OF STREAMFLOW PROCESSES AT DIFFERENT TIME SCALES

The analysis and modeling of streamflow processes has attracted the at tention of water resources specialists for several decades. A number o f models have been suggested in the past for representing seasonal and annual streamflow processes. The topic addressed in this paper center s around the compatibility of stochastic models of streamflow at diffe rent time scales. More specifically, given a model for monthly flows, the models for the processes obtained by aggregation, i.e., models for bimonthly, quarterly, etc., time scales, are derived. Likewise, param eter space and covariance properties of such derived processes as well as the relationship of such properties of different time scales are g iven. These concepts are applied to modeling streamflow of the Niger R iver. The developments are restricted to the family of periodic autore gressive moving average (PARMA) processes. For instance, it was found that monthly flows based on the PARMA(2, 1) process leads to PARMA(2, 2) bimonthly flows and stationary ARMA(2, 2) annual flows. Furthermore , applications to modeling the Niger River flows suggest that one can reproduce the seasonal and annual second-order statistics without usin g disaggregation if PARMA models are used for modeling the seasonal fl ows.