Data-Driven Surrogate Modeling with Satellite Data Assimilation: Advancing Basin-Scale Hydrology for Water Balance Simulation

 

Hydrological modeling is essential for understanding water balance dynamics, yet physical models often face limitations such as computational inefficiency, insufficient representation of complex processes, and challenges in integrating diverse data sources. To address these limitations, data-driven models offer enhanced predictive capabilities, scalability, and real-time analysis potential. In this study, a Long Short-Term Memory (LSTM) model was developed to simulate the water balance using open-source ERA-5 reanalysis data, trained with outputs from the Noah-MP land surface model conducted over Thailand as a case study. The trained LSTM model was subsequently transferred to other basins with varying land use, topography, and climatic conditions, enabling an evaluation of its adaptability across diverse environments. Seasonal performance was assessed to understand the model's sensitivity to climatic variability. To enhance the accuracy of water balance predictions, satellite datasets, including GRACE-derived terrestrial water storage, GLEAM-derived evapotranspiration, and SMAP-derived surface soil moisture, were assimilated into the data-driven model, improving its representation of hydrological processes. Model performance was assessed using observations, yielding notable results: correlation coefficients (R) of 0.98, 0.89, 0.99, and 0.99; and RMSE values of 16.77, 8, 5.2, and 0.01 for runoff, evaporation, groundwater, and soil moisture, respectively. This study highlights the potential of combining data-driven approaches and satellite data assimilation to improve water balance modeling,  providing accurate hydrological predictions across regions with diverse landscapes and climatic regimes.