Advancing 9-Month Hydrologic Forecasts for Large Transboundary Basins: A Case Study in the Great Lakes Region

Hydrologic forecasting in large, transboundary basins such as the Laurentian Great Lakes remains a significant challenge due to complex hydrological processes, extensive surface water coverage, and widespread ungauged regions. These factors limit the predictive skill of existing models and constrain effective operational water resource management. This study evaluates and compares two modeling approaches for seasonal runoff prediction: a data-driven Long Short-Term Memory (LSTM) neural network model and the process-based Large Basin Runoff Model (LBRM). Using the Climate Forecast System Reanalysis (CFSR) as climate forcing from 1979 to 2023, both LSTM and LBRM models were developed and tested to simulate runoff across the entire Great Lakes basin. Their seasonal forecasting performance was then assessed using archived 9-month forecasts from the Climate Forecast System (CFS) as input. Additionally, total regional runoff was evaluated from a water balance perspective to assess model effectiveness for seasonal water supply forecasting. This research aims to refine hydrologic modeling frameworks using the Great Lakes as a testbed for seasonal-to-annual water supply forecasting. The results offer insights into the comparative strengths of data-driven and process-based models and highlight their potential integration for improving forecast accuracy. Ultimately, this work supports the development of more reliable forecasting tools for operational water management in the Great Lakes and other large transboundary basins worldwide.