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...

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Bibliographic Details
Published in:Hydrological sciences journal 2023-03, Vol.68 (4), p.614-629
Main Authors: Bahrami, Maziyar, Talebbeydokhti, Nasser, Rakhshandehroo, Gholamreza, Nikoo, Mohammad Reza, Adamowski, Jan Franklin
Format: Article
Language:English
Subjects:
Case studies
Daily precipitation
Data assimilation
Data collection
Datasets
Frameworks
fusion
Genetic algorithms
Ground stations
Hydrology
Markov chains
Mean daily precipitation
Modelling
Moisture effects
Monte Carlo simulation
Optimization models
ORNESS weighting method
particle filter
Performance measurement
Precipitation
Procedures
Runoff
SAC-SMA model
satellite precipitation
Soil moisture
Statistical methods
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Summary: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).
ISSN:0262-6667
2150-3435
DOI:10.1080/02626667.2023.2180375