Advancing Streamflow Modelling: Bias Removal in Physically-Based Models with the Long Short-Term Memory Networks (LSTM) Algorithm

Traditional hydrological models have long served as the standard for predicting streamflow across temporal and spatial domains. However, a persistent challenge in modelling lies in mitigating bias inherent in streamflow estimation due to both random and systemic errors in the employed model. Removal of this bias is pivotal for effective water resources management and resilience against extreme events, especially amidst evolving climate conditions. An innovative solution to address this challenge involves the integration of hydrological models with deep learning methods, known as hybridisation. Long Short-Term Memory networks (LSTM), have emerged as a promising and efficient approach to enhancing streamflow estimation. This study focuses on coupling LSTM with a physically distributed model, Wflow_sbm, to serve as a post-processor aimed at reducing modelling errors. The coupled Wflow_sbm-LSTM model was applied to the Boyne catchment in Ireland, utilising a dataset spanning two decades, divided into training, validation, and testing sets to ensure robust model evaluation. Predictive performance was rigorously assessed using metrics like Modified Kling-Gupta Efficiency (MKGE) and Nash-Sutcliffe Efficiency (NSE), with observed streamflow discharges as the target variable. Results demonstrated that the coupled model outperformed the best-calibrated Wflow_sbm model in the study catchment based on the performance measures. The enhanced prediction of extreme events by the coupled Wflow_sbm-LSTM model strengthens the case for its integration into an operational river flow forecasting framework. Significantly, Wflow is endorsed by the National Flood Forecast Warning Service (NFFWS) in Ireland as a recommended model for streamflow simulations, specifically designed for fluvial flood forecasting. Consequently, our proposed Wflow_sbm-LSTM coupled model presents a compelling opportunity for integration into the NFFWS. With demonstrated potential to achieve precise streamflow estimations, this integration holds promise for significantly enhancing the accuracy and effectiveness of flood predictions in Ireland.