An Experiment on Risk-Based Decision-Making in Water Management Using Monthly Probabilistic Forecasts

Crochemore, Louise ; Ramos, Maria-Helena ; Pappenberger, Florian ; van Andel, Schalk Jan ; Wood, Andrew W.

Année de publication
2016
Résumé
The use of probabilistic forecasts is necessary to take into account uncertainties and allow for optimal risk-based decisions in streamflow forecasting at monthly to seasonal lead times. Such probabilistic forecasts have long been used by practitioners in the operation of water reservoirs, in water allocation and management, and more recently in drought preparedness activities. Various studies assert the potential value of hydrometeorological forecasting efforts, but few investigate how these forecasts are used in the decision-making process. Role-playing games can help scientists, managers, and decision-makers understand the extremely complex process behind risk-based decisions. In this paper, we present an experiment focusing on the use of probabilistic forecasts to make decisions on reservoir outflows. The setup was a risk-based decision-making game, during which participants acted as water managers. Participants determined monthly reservoir releases based on a sequence of probabilistic inflow forecasts, reservoir volume objectives, and release constraints. After each decision, consequences were evaluated based on the actual inflow. The analysis of 162 game sheets collected after eight applications of the game illustrates the importance of leveraging not only the probabilistic information in the forecasts but also predictions for a range of lead times. Winning strategies tended to gradually empty the reservoir in the months before the peak inflow period to accommodate its volume and avoid overtopping. Twenty percent of the participants managed to do so and finished the management period without having exceeded the maximum reservoir capacity or violating downstream release constraints. The role-playing approach successfully created an open atmosphere to discuss the challenges of using probabilistic forecasts in sequential decision-making.

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