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Title

An experiment on risk-based decision-making in water management using monthly probabilistic forecasts

Abstract

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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document

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http://n2t.net/ark:/85065/d7ht2qz2

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eng

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geoscientificInformation

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publication

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2016-01-01T00:00:00Z

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publication

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2016-04-01T00:00:00Z

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Copyright 2016 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Law (17 USC, as revised by P.L. 94-553) does not require the Society's permission. Republication, systematic reproduction, posting in electronic form on servers, or other uses of this material, except as exempted by the above statements, requires written permission or license from the AMS. Additional details are provided in the AMS Copyright Policies, available from the AMS at 617-227-2425 or amspubs@ametsoc.org. Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published work.

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OpenSky Support

organisation name

UCAR/NCAR - Library

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PO Box 3000

Boulder

80307-3000

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opensky@ucar.edu

web address

http://opensky.ucar.edu/

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