Recent work in on-line hydrological forecasting has utilized the Kalman-Bucy filter for state (discharge) forecasting. One drawback of using these algorithms has been restrictions related to the size of the state vector due to computation and computer limitations; resulting in limiting hydrological applications to small headwater catchments. Forecasts of headwater catchments are of limited use to agencies such as the US National Weather Service which routinely have catchments with between 10 and 100 forecast points. This paper sets forth the methodology that allows forecasting of large systems by partitioning the system into subsystems where the filtering of subsystems is performed in parallel. The interactions between the partitioned subsystems are accounted for by supplementing the noise processes. An added institutional advantage to the partitioning approach is the ability for forecasting agencies to set up local forecast centres that are coordinated through a regional centre. This is especially important in large river basins which experience localized flooding on tributary streams. Often regional forecast centres are more concerned with the main river and ignore (or their models do not have the ability to forecast) localized flooding.