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How does the quantification of uncertainties affects the quality and value of flood early warning systems?

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In an operational context, efficient decision-making is usually the ultimate objective of hydrometeorological forecasts. Because of the uncertainties that lay within the forecasting process, decisions are subject to uncertainty. A better quantification of uncertainties should provide better decisions, which often translate into optimal use and economic value of the forecasts. Six Early Warning Systems (EWS) based on contrasted forecasting systems are constructed to investigate how the quantification of uncertainties affects the quality of a decision. These systems differ by the location of the sources of uncertainty, and the total amount of uncertainty they take into account in the forecasting process. They are assessed with the Relative Economic Value (REV), which is a flexible measure to quantify the potential economic benefits of an EWS. The results show that all systems provide a gain over the case where no EWS is used. The most complex systems, i.e. those that consider more sources of uncertainty in the forecasting process, are those that showed the most reduced expected damages. Systems with better accuracy and reliability are generally the ones with higher REV, even though our analysis did not show a clear-cut relationship between overall forecast quality and REV in the context investigated.
Journal of Hydrology, Vol. 551, 365–373 (2017)
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Forecast quality , Relative economic value , Uncertainty estimation , Ensemble prediction , Data assimilation , Ensemble Kalman Filter , Multimodel
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