Integrated Multisource Data Assimilation and NSGA-II Multiobjective Optimization Framework for Streamflow Simulations

Given the importance of input data, particularly precipitation, in hydrologic modeling for streamflow simulation, there has been growing emphasis on developing frameworks that harness multiple data sources concurrently to achieve more precise results. In the proposed framework of this study, which relies on the integrated capabilities of the multiobjective optimization model [nondominated sorting genetic algorithm-II (NSGA-II)], the ensemble Kalman filter data assimilation method, and data fusion, rainfall data from multiple sources are incorporated. The utilized framework leads to an improvement in the mean absolute relative error (MARE) index of streamflow simulation results. The innovation of the proposed methodology is the calculation of optimal weights corresponding to the simulated runoff time-series in the fusion model. This is accomplished through a competitive process among a multitude of optimized scenarios simulated within the framework provided. MARE as the main index identified in the objective functions and standard deviation, centered root mean square distance, and the correlation coefficient as auxiliary indices have been considered in this process. In this framework, satellite-based and in situ precipitation data sets are used as the forcing data. The main challenge has been to choose the greatest scenario for fusion among the selected scenarios, which the proposed methodology has overcome. The performance of the suggested methodology is demonstrated for the Siakh-Darengon catchment located in the Fars Province of Iran. According to the results, an average of 14.07% improvement in the MARE index has been achieved after applying the proposed methodology. By utilizing the proposed method, satellite-based rainfall data are integrated alongside ground-based rainfall data in the flood modeling process, resulting in enhanced accuracy in simulation outcomes within the utilized watersheds.