Skill improvement of snow-dominated reservoir inflow forecasts using seasonal weather predictions

We present the post-processing technique for the operational ensemble forecasting system (EFS) currently applied to the Cheboksary reservoir on the Volga River in Russia. The operational forecasting system is built around the ECOMAG semi-distributed hydrological model and has shown to produce reliable forecasts of spring snowmelt water inflow into the reservoir on lead-times up to four months ahead (Gelfan et al., 2018). We propose the improvement of the mean reservoir monthly inflow forecast skill by constructing cumulative distributions (CDF) of the observed streamflow conditioned on the predicted streamflow from the EFS and observed mean monthly air temperature and precipitation. We overcome the limitation of short time-series of the observed variables by multivariate modelling procedure allowing for the time-series extension. The extended time-series are then classified into 64 categories each containing the unique combination of the predictors by their quartile values, and the observed monthly inflow CDFs are constructed. The improved operational forecast CDF is consequently picked from the obtained 64 CDF classes by defining the appropriate CDF class from the combination of the raw ensemble forecast and any weather prediction available. The proposed technique was assessed by using the SL-AV weather model (Khan, 2011; Tolstykh, 2017) monthly temperature and precipitation hindcasts for the evaluation period of 1982 – 2010. The forecasts were benchmarked against climate and observed (perfect) weather forecast and have shown improvement in terms of reliability and resolution.

The research is supported by the Russian Science Foundation, project no. 17-77-30006.

References:

Gelfan, A., Moreydo, V., Motovilov, Y., & Solomatine, D. P. (2018). Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios. Hydrology and Earth System Sciences, 22(4). https://doi.org/10.5194/hess-22-2073-2018

Tolstykh, M., Shashkin, V., Fadeev, R., and Goyman, G.: Vorticity-divergence semi-Lagrangian global atmospheric model SL-AV20: dynamical core, Geosci. Model Dev., 10, 1961–1983, https://doi.org/10.5194/gmd-10-1961-2017, 2017

Khan V.M., Kryzhov V.N., Vilfand R.M., Tishchenko V.A., Bundel A.Y. Multimodel approach to seasonal prediction. Russian Meteorology and Hydrology. 2011. Т. 36. № 1. С. 11-17.