Data Assimilation in Hydrological Models: Methods, Challenges and Emerging Trends

The integration of multi-source data represents a defining trend in hydrological science, while the comprehensive quantification and characterization of inherent uncertainties in hydrological model prediction remains imperative. Data assimilation (DA) techniques offer a rigorous framework for integrating multi-source observational data with model simulations through systematic uncertainty characterization, thereby enhancing predictive accuracy while providing quantitative uncertainty estimates. This study systematically synthesizes and extracts the research hotspots and cutting-edge trends of DA within the hydrology domain. Specifically, from the perspectives of model structure, parameters, and states, it categorizes the development of data assimilation techniques in hydrology into system identification, parameter estimation, and state estimation. The research identifies several key challenges confronting the field of hydrological DA, including inherent nonlinear characteristics of hydrological processes, insufficient spatial coverage and limited availability of observational data, necessity for substantial modifications to existing hydrological models for DA compatibility, difficulties in quantifying errors within raw datasets, and computational complexity arising from high-dimensional state spaces during assimilation. Finally, using the Kalman filter as an illustrative example, the study demonstrates the concrete application of DA. It is proposed that the integration of deep learning with DA, coupled with the joint estimation of parameters and states, represents the promising and breakthrough directions for the future development of DA methodologies in hydrological research.