Development of multimodel-based hydrologic outlook for India

Abstract

Real-time monitoring and early warning systems for hydrological variables are essential for the 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 forecast, and ensemble of three hydrological models (VIC, Noah-MP, and H08), we examine the potential of meteorological forecast for the development of a hydrologic outlook at a short-to-subseasonal lead time. We evaluate the role of multivariate bias correction that ensures co-variability of precipitation and temperature in the monsoonal climate on 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 overall wet bias in precipitation and warm bias in maximum and minimum temperatures, which was significantly improved after the multivariate bias correction. As the bias correction of meteorological forecast and post-processing 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 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.