Articles | Volume 22, issue 4
Research article
04 Apr 2018
Research article |  | 04 Apr 2018

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

Alexander Gelfan, Vsevolod Moreydo, Yury Motovilov, and Dimitri P. Solomatine

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Cited articles

Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geogr., 36, 480–513,, 2012. 
Arnal, L., Wood, A. W., Stephens, E., Cloke, H. L., and Pappenberger, F.: An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity, J. Hydrometeorol., 18, 1715–1729,, 2017. 
Avakyan, A. B.: Volga-Kama cascade reservoirs and their optimal use, Lakes Reserv. Res. Manage., 3, 113–121, 1998. 
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,, 2016. 
Borsch, S. and Simonov, Y.: Operational Hydrologic Forecast System in Russia, in: Flood Forecasting: A Global Perspective, Academic Press, London, UK, 169–181, 2016. 
Short summary
We describe a forecasting procedure that is based on a semi-distributed hydrological model using two types of weather ensembles for the lead time period: observed weather data, constructed on the basis of the ESP methodology, and synthetic weather data, simulated by a weather generator. We compare the described methodology with the regression-based operational forecasts that are currently in practice and show the increased informational content of the ensemble-based forecasts.