Articles | Volume 21, issue 7
https://doi.org/10.5194/hess-21-3915-2017
https://doi.org/10.5194/hess-21-3915-2017
Research article
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31 Jul 2017
Research article | Highlight paper |  | 31 Jul 2017

An intercomparison of approaches for improving operational seasonal streamflow forecasts

Pablo A. Mendoza, Andrew W. Wood, Elizabeth Clark, Eric Rothwell, Martyn P. Clark, Bart Nijssen, Levi D. Brekke, and Jeffrey R. Arnold

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Cited articles

Abdi, H.: Partial least squares regression and projection on latent structure regression, Wiley Interdiscip. Rev. Comput. Stat., 2, 97–106, https://doi.org/10.1002/wics.051, 2010.
Akaike, H.: A new look at the statistical model identification, IEEE Trans. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Anderson, E.: National Weather Service River Forecast system – snow accumulation and ablation model, NOAA Tech. Memo. NWS HYDRO-17, 217 pp., 1973.
Beckers, J. V. L., Weerts, A. H., Tijdeman, E., and Welles, E.: ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction, Hydrol. Earth Syst. Sci., 20, 3277–3287, https://doi.org/10.5194/hess-20-3277-2016, 2016.
BPA: 2010 Level Modified Streamflow: 1928–2008, DOE/BP-4352, Seasonal Volumes and Statistics, 2011.
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Short summary
Water supply forecasts are critical to support water resources operations and planning. The skill of such forecasts depends on our knowledge of (i) future meteorological conditions and (ii) the amount of water stored in a basin. We address this problem by testing several approaches that make use of these sources of predictability, either separately or in a combined fashion. The main goal is to understand the marginal benefits of both information and methodological complexity in forecast skill.