| description abstract | Public water utilities face uncertain decisions every day in their efforts to meet drinking water needs of customers. Real-time decision-support tools (DST) are often used by water managers to solve a variety of water management challenges, including meeting customers’ demands, forecasting floods, and developing reservoir operating rules. The incorporation of seasonal forecasting can improve operational decision making by explicitly including uncertainties that affect these near-term decisions. This study presents an application of DST that incorporate rainfall/streamflow uncertainties, seasonal demand forecasts, and system operational constraints to assist utility decision-makers. Large-scale climate information is used in monthly precipitation forecasts using a hidden Markov-chain model. An ad hoc seasonal demand forecasting model considers weather conditions explicitly and socioeconomic factors implicitly. The seasonal system operation is modeled as a mixed-integer optimization problem that aims at minimizing operational costs. It embeds the flexibility of incorporating operational rules at different components, e.g., surface water treatment plants, desalination facilities, and groundwater pumping stations. The proposed framework is illustrated for a water supply agency in the southeastern United States, Tampa Bay Water. The use of the tool is demonstrated in providing operational guidance for taking a large storage reservoir offline for a two-week period to conduct a required inspection. The results provided insights for the best time to take the reservoir offline and yet meet an operational objective of filling the reservoir by October 1. Although this application is illustrated for Tampa Bay Water, it demonstrates the use of DST for regional water management in other areas. | |
