Development of a Multimodel-Based Hydrologic Outlook for India

Abstract Real-time monitoring and early warning systems for hydrological variables are essential for decision-making for managing water resources and agricultural activities. Notwithstanding the considerable progress in operational weather and climate forecast in India, efforts to develop a multimodel-based hydrological outlook utilizing the meteorological forecast have been lacking. Here, using gridded observations, meteorological forecasts, and an ensemble of three hydrological models [Variable Infiltration Capacity (VIC), Noah land surface model with multiparameterization options (Noah-MP), and H08], we examine the potential of meteorological forecasts for the development of a hydrologic outlook at a short-to-subseasonal lead times. We evaluate the role of multivariate bias correction that ensures the covariability of precipitation and temperature in the monsoonal climate in the prediction skills of hydrologic outlook components (precipitation, temperature, evapotranspiration, runoff, soil moisture, and streamflow). The raw forecast from the Extended Range Forecast System (ERFS) showed an overall wet bias in precipitation and a warm bias in maximum and minimum temperatures, which were significantly improved after the multivariate bias correction. As the bias correction of meteorological forecasts and postprocessing of streamflow resulted in the best prediction skills, we used it to develop the hydrologic outlook. The developed hydrologic outlook demonstrated reasonable forecast skills at a 1–30-day lead time for extreme dry and wet conditions. The multimodel-based hydrologic outlook can assist the decision-making in water resources and agriculture in India.