A Bayesian hierarchical model combination framework for real-time daily ensemble streamflow forecasting across a rainfed river basin

Abstract

The frequent occurrence of floods during the rainy season is one of the threats in rainfed river basins, especially in river basins of India. This study implemented a Bayesian hierarchical model combination (BHMC) framework to generate skillful and reliable real-time daily ensemble streamflow forecast and peak flow and demonstrates its utility in the Narmada River basin (NRB) in Central India for the peak monsoon season (July-August). The framework incorporates information from multiple sources (e.g., deterministic hydrological forecast, meteorological forecast, and observed data) as predictors. The forecasts were validated with a leave-1-year-out cross-validation using accuracy metrics such as BIAS and Pearson correlation coefficient (R) and probabilistic metrics such as Continuous ranked probability skill score (CRPSS), probability integral transform (PIT) plots, and the average width of 95% confidence intervals (AWCI) plots. The results show that the BHMC framework can increase the forecast skill by 40% and reduce absolute bias by at least 28% compared to the raw deterministic forecast from a physical model, the Variable Infiltration Capacity (VIC) model. In addition, PIT and AWCI show that the framework can provide sharp and reliable streamflow forecast ensembles for short lead times (1-3-day lead time) and provide useful skills beyond up to 5-day lead time. These will be of immense help in emergency and disaster preparedness.