Reconstruction and Prediction of Spatio-Temporal Temperature and Salinity Fields in Controlled River Using Data-Driven Deep Fusion

Accurately estimating the temperature and salinity structure of lakes or reservoirs is crucial for understanding terrestrial hydrological processes and pollutant transport path-ways. However, key parameters for solute transport models in hydrodynamic systems are difficult to obtain directly and often require inversion simulations involving multi-source solute parameters. This study addresses the challenges of multi-source heterogeneous data assimilation and the optimization of training data distribution for alternative models. We develop a hybrid algorithm that solves data assimilation issues for multi-source heterogeneous data in reactive solute transport inversion simulations. By combining posterior inference of states with the prior distribution of parameters, we propose a novel coupled paradigm and explore the impact of identifying characteristic parameters across multiple scenarios and the performance advantages of surrogate models.