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Model identification for hydrological forecasting under uncertainty

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Abstract

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.

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Acknowledgements

We particularly like to thank the editors for their invitation to contribute this discussion paper and the three reviewers for very constructive criticism that helped to improve the quality of the paper and clarify important points. Partial support for the authors was provided by SAHRA under NSF-STC grant EAR-9876800, and the National Weather Service Office of Hydrology under grant numbers NOAA/NA04NWS4620012, UCAR/NOAA/COMET/S0344674, NOAA/DG 133W-03-SE-0916.

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Wagener, T., Gupta, H.V. Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Ris Assess 19, 378–387 (2005). https://doi.org/10.1007/s00477-005-0006-5

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