Influence of Bias Correction of Meteorological and Streamflow Forecast on Hydrological Prediction in India

The efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecasts and streamflow postprocessing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root-zone soil moisture in the Narmada basin (drainage area: 97 410 km²), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June–September) season for the 2000–18 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1–3-day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root-mean-square error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1–3-day lead time. We used empirical quantile mapping (EQM) to bias-correct precipitation forecasts. The bias correction of precipitation forecasts resulted in significant improvement in the precipitation forecast skill. Runoff and root-zone soil moisture forecasts were also significantly improved due to bias correction of precipitation forecasts where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecasts did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecasts performs better than the streamflow forecasts simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecasts and postprocessing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40% to 5%).