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

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.
