Reliable monthly and seasonal streamflow predictions are essential for optimal planning of water resources, particularly for reservoir operation and planning applications. Streamflow predictions can also improve water use efficiency and provide early drought and flood warning. The importance of streamflow forecasting is rising with climate change, causing more frequent and hazardous flood and drought events.

Reservoir operators typically determine flow releases by assessing the available storage and demand and then applying a rule?of?thumb. Thus, in spite of numerous previous and current computer modelling studies, the use of computer models in real?time reservoir operation on a district level is virtually nonexistent. The resulting “water savings” offer the opportunity to increase river flows, making greater volumes of water available to irrigated agriculture to reduce drought risks, or support irrigation sector expansion that meets all water licenses and in?stream water?quality objectives.

The project is aimed to establish and algorithm and computer software for generation of multiple time series of data which represent processes that are closely correlated and auto-correlated (i.e. data in time step i are correlated to the data in time steps i-1, i-2,i-3, etc.). This project is of significant importance to the company.  It represents significant improvement of our earlier developments in this field.