Abstract

Abstract. Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

Highlights

  • Hydropower production planning typically requires inflow forecasts for reservoirs at different lead times

  • The goal of this study is to evaluate the potential of dynamical streamflow prediction” (DSP) in terms of predictability improvement for long-term streamflow forecasting, compared to historical streamflow prediction” (HSP) and extended streamflow prediction” (ESP)

  • Raw forecasts from European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 suffer from biases

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Summary

Introduction

Hydropower production planning typically requires inflow forecasts for reservoirs at different lead times. Sub-seasonal to seasonal ensemble meteorological forecasts produced by dynamic climate models have undergone constant improvements, and it is worth assessing their usefulness for long lead-time inflow forecasting. The DEMETER (Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction; Palmer et al, 2004) database and ESP for hydrological forecasting on a watershed in Ohio during summer They found that the multimodel approach is more efficient than a single climate model. Their results indicate that CFSv2 improves hydrological forecasting performances for the 1-month lead time, whereas CFSv1-based forecasts are not very efficient He et al (2016) compared the performance of climatology (ESP) and CFSv2 for a single watershed in the Sierra Nevada.

Watersheds
Current operational streamflow forecasting system
Forecast quality assessment
Bias characterisation and correction
Bias-corrected meteorological ensemble forecasts compared against climatology
Monthly inflow volume forecasts
Conclusion
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