Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India

Abstract

Abstract. Developing an ensemble hydrological prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrological ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River Basin as a test bed. We use the variable infiltration capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada Basin. Streamflow prediction skills of the ERFS forecast were examined for the period 2003–2018 at 1–32 d lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1–10 d lead for the summer monsoon (June–September) 2019–2020. The ERFS forecast underestimates extreme precipitation against the observations compared to the GEFS forecast during the summer monsoon of 2019–2020. However, both forecast products show better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performs better than the ERFS during 2019–2020. The performance of GEFS-based ensemble streamflow forecast declines after 5 days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1–5 d lead, which can be utilized in streamflow prediction. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India.