A fusion-based data assimilation framework for runoff prediction considering multiple sources of precipitation

A fusion-based framework, in which a particle filter Markov chain Monte Carlo (PFMCMC) data assimilation method was coupled with the hydrological Sacramento Soil Moisture Accounting Model (SAC-SMA), was developed to improve the model's capacity to predict one-day-ahead runoff. A case study was applied where mean daily precipitation from multiple sources served as forcing data in the data assimilation procedure, while ground station and multiple bias-corrected satellite-based precipitation datasets served as precipitation input datasets. The model training period used six years (2002-2007) of data to determine optimal weights through a genetic algorithm optimization model, while two years (2008-2009) were used to test the model. The proposed framework, applied to a real case study, improved SAC-SMA runoff prediction accuracy by incorporating precipitation datasets from multiple sources in the data assimilation procedure. On average, the PFMCMC-based data assimilation procedure led to a 13.7% improvement in SAC-SMA model performance metrics (NSE, MAB, RMSE, RMSRE, RMRE).

Automated summary

A fusion-based framework was developed by coupling a particle filter Markov chain Monte Carlo (PFMCMC) data assimilation method with the hydrological Sacramento Soil Moisture Accounting Model (SAC-SMA) to improve one-day-ahead runoff prediction. Mean daily precipitation from multiple sources was used as forcing data, while ground station and multiple bias-corrected satellite-based precipitation datasets served as precipitation input datasets. The proposed framework improved SAC-SMA runoff prediction accuracy by incorporating precipitation datasets from multiple sources in the data assimilation procedure, leading to a 13.7% improvement in SAC-SMA model performance metrics (NSE, MAB, RMSE, RMSRE, RMRE).