EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Informed water infrastructure design: improving coupled dam sizing and operation by streamflow forecasts

Andrea Castelletti1, Federica Bertoni1, Matteo Giuliani1, and Patrick Reed2
Andrea Castelletti et al.
  • 1Politecnico di Milano, Dept. Electronics, Information, and Bioengineering, Milano, Italy (;;
  • 2Cornell University, Dept. Civil and Environmental Engineering, Ithaca, NY, USA (

There is a large body of recent research that is capitalizing on the improved skill of state-of-the-art hydroclimatic services for investigating their value in informing water reservoir operations. Yet, the potential value of these services in informing infrastructure design is still unexplored. In this work, we investigate the added value of hydroclimatic services in the planning of water reservoirs, composed of the joint design of the infrastructure’s size and its operations informed by streamflow forecasts. We demonstrate the potential of our approach through an ex-post design analysis of the Kariba dam in the Zambezi river basin, which is the largest man-made reservoir in Africa. The reservoir is operated for hydropower production and irrigation supply. Specifically, we search for flexible operating policies informed by streamflow forecasts that allow the design of smaller and less costly reservoirs with respect to solutions that do not rely on forecast information. This requires selecting the most informative forecast lead times to use in the dam design phase, which depends on both infrastructural reservoir characteristics and tradeoffs across performance objectives. After estimating the value of perfect forecasts, we analyze its sensitivity with respect to using imperfect synthetic forecasts characterized by different biases. The results show that informing the infrastructure design with perfect streamflow forecasts allows reducing capital costs by 20% with respect to a baseline solution not informed by any forecast, while maintaining the same performance in terms of hydropower production and water supply. Forecast overestimation results in the most critical synthetic forecast bias, reducing their value by 8%. Moreover, our analysis show that forecast value is highly sensitive to reservoir size and operational tradeoffs, ultimately representing a valuable tool for supporting the ongoing planning of 3,700 major dams worldwide.

How to cite: Castelletti, A., Bertoni, F., Giuliani, M., and Reed, P.: Informed water infrastructure design: improving coupled dam sizing and operation by streamflow forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15835,, 2020


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