Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Denaro, Simona, Anghileri, Daniela, Giuliani, Matteo and Castelletti, Andrea (2017) Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data. Advances in Water Resources, 103, 51-63. (doi:10.1016/j.advwatres.2017.02.012).

Abstract

Water reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used.

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Contributors

Author: Daniela Anghileri

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