An integrated approach that utilizes climate information based probabilistic streamflow forecasts to improve short-term water allocation and to develop operational rule curves is proposed. The approach encompasses a framework for prmoting participatory water allocation process, where users could express their potential demand for water through statements that cover quantity needed at a particular reliability, the associated willingness to pay, and compensation needed in the event of contract non-performance. Community priorities can also be accommodated, and a system of contracts can be designed that meets multiple needs with specified reliability and priorities, contingent on a probabilistic inflow forecast. These contracts can be used to allocate water each year above and beyond long term contracts that may have precedence.

Application of this framework for two basins in contrasting settings with one being semi-arid (Ceara, North East Brazil) and the other being Angat, Philippines (Angat, Philippines) provide valuable information in utilizing climate forecasts for improving reservoir management. By performing retrospective analyses that combines streamflow forecasts with the water allocation framework, we show that considerable reduction in system losses (spill and evaporation) could be achieved resulting in increased reservoir yields by utilizing climate forecasts for operational reservoir systems management. The analyses also show that incorporating end of season storage ensures the necessary storage for operating the reservoirs during seasons with limited predictability. Importance of updating the climate forecasts on a monthly basis and its utility in improving hydropower generation are also demonstrated. For years with initial storage conditions binding the allocation for irrigation use, we investigate the use of updated forecasts for pursuing delayed irrigation season. Further, analyzing the system performance under different scenarios of storage and demand, we show that the utility of climate information based reservoir inflow forecasts is more pronounced for systems with low storage to demand ratio. As challenges in implementing these scientific developments, we emphasize the importance of institutional setting and the relevant policy instruments that will promote climate information based risk management strategies.