Sub‐Seasonal Prediction of Drought and Streamflow Anomalies for Water Management in India

Abstract

Meteorological and hydrologic prediction at short to sub-seasonal scales is essential for reservoir operations to mitigate droughts. We examine the skills in the meteorological forecast from the Subseasonal Experiment (SubX) and Extended Range Forecast System (ERFS) for precipitation, maximum and minimum temperatures at 1, 7, 15, and 30 days lead. We bias-corrected meteorological forecasts using the multivariate bias correction method for hydrologic prediction. The Variable Infiltration Capacity model was used to simulate total runoff and root-zone soil moisture for India. We also developed a streamflow forecast for the five major river basins that have large reservoirs. Bias correction of meteorological forecast (precipitation, maximum and minimum temperatures) resulted in a considerable improvement in hydrologic and meteorological forecast skills. The Environmental Modeling Center (EMC) model from the SubX provides either better or equal forecast skills for the raw meteorological forecast compared to ERFS, which is an operational product in India. We examined the forecast skills of the meteorological and hydrological products for the two major droughts that occurred recently. We find that most forecast models effectively captured the onset, peak, and termination of the North Indian drought in 2015–2016 and the South Indian drought in 2016–2017 at a 30-day lead. Bias correction of the meteorological forecast improved the streamflow forecast for the selected drought event upstream of the major reservoirs. The EMC model showed better forecast skills for the two major droughts than other forecast products. Overall, the SubX products show potential for short-to-sub-seasonal scale hydrologic prediction that can assist water management in India.