A Bayesian Hierarchical Model Combination Framework for Real‐Time Daily Ensemble Streamflow Forecasting Across a Rainfed River Basin. Issue 12 (20th December 2022)

• Record Type: Journal Article
• Authors: Ossandón, Álvaro; Rajagopalan, Balaji; Tiwari, Amar Deep; Thomas, Thomas; Mishra, Vimal
• Is Part Of: Earth's future. Volume 10:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: short‐time forecasting -- Bayesian hierarchical model combination modeling -- rainfed basins -- ensemble forecasting -- daily monsoon season streamflow; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2022EF002958
• Languages: English
• ISSNs: 2328-4277

Abstract

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 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, probability integral transform (PIT) plots, and the average width of the 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 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. Plain Language Summary: This study implemented a Bayesian hierarchical based probabilistic forecast modeling framework to generate skillful and reliable real‐time daily ensemble streamflow forecast and peak flow at multiple locations on a river network. This framework was demonstrated in the Narmada River basin network 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 results show that compared to the raw deterministic forecast, the Bayesian framework can increase the forecast skill by 40%, reduce absolute bias by at least 28%, and provide sharp and reliable streamflow forecast ensembles at 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. Key Points: We develop a Bayesian model combination framework for real‐time streamflow forecasting The framework can be easily implemented in other major rainfed basins worldwide The framework can provide reliable streamflow forecast for short lead times