Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework

The National Weather Service (NWS) operates the National Water Model (NWM) to provide continental-scale streamflow forecasting across the United States. Despite the broad scope of NWM, it faces limitations in delivering operational-level predictions. To overcome these limitations, the NWS embarked on development of the Next Generation Water Resources Modeling Framework (NextGen). However, a key shortcoming of the NextGen and NWM is the lack of robust data assimilation (DA) step. This study provides a DA module that incorporates the Ensemble Kalman Filter (EnKF), and the Particle Filter (PF) for use within the NextGen framework. The effectiveness of the developed module is evaluated by assimilating the in-situ observations to the Conceptual Functional Equivalent model, a simplified version of the current NWM, demonstrating the first advanced DA application on this model. The results show that both DA methods effectively enhance the performance of the model prediction, while the PF outperforms the EnKF. • Bayesian data assimilation module is developed for the NextGen framework. • The Basic Model Interface (BMI) is used to connect the module to the framework. • The developed module is tested on a hydrologic model with real case studies. • The hydrologic model performance is improved using the developed module.