With low energy costs, dam and hydroelectric plants owners face the challenge of meeting operational and safety requirements at an optimal cost. The operation of hydropower facilities involves consideration of many diverse and often divergent interests including safety, power generation, flood and drought prevention, low flow augmentation, navigation, recreation, and water supply. Accurate and reliable inflow forecasting tools are critical for operators to balance competing interests and make informed decisions on the efficient use of water resources and their facilities. Understanding the uncertainties of the inflow forecasting will increase the efficient use of water resources and allow better dam safety analysis.
This study will first review and document the existing and emerging techniques and methods estimating inflow, both deterministic and probabilistic. The second and main focus will be to review what is currently done in the evaluation of the uncertainties of inflow. This review will also include what uncertainties to consider, how to calculate the uncertainties, what are the impacts of the uncertainties, along with how to communicate these uncertainties and their impact to stakeholders, upper level management, and staff.
Here are some examples of the questions that the review should help answer:
- What is the state of estimating inflows?
- Types of models used
- What are the different types and magnitude of uncertainties affecting inflow estimation methods?
- What percentage of the overall pool uncertainty is caused by various factors such as rainfall, runoff, routing and operations in various scenarios?
- How can practitioners account for uncertainty without multiplying their work unreasonably?
- How can practitioners communicate uncertainty to stakeholders, executives, and staff?
The final report will provide a systematic review and classification of current state-of-the-art techniques and methods for inflow estimation with the main focus on the evaluation of the uncertainties affecting such estimations.