Enhanced Continental Runoff Prediction through Differentiable Muskingum-Cunge Routing (δMC-CONUS-hydroDL2)

Recent advancements in flow routing models have enabled learning from big data using differentiable modeling techniques. However, their application remains constrained to smaller basins due to limitations in computational memory and hydrofabric scaling. We propose a novel methodology to scale differentiable river routing from watershed (HUC10) to continental scales using the δMC-CONUS-hydroDL2 model. Mimicking the Muskingum-Cunge routing model, this approach aims to enhance flood wave timing prediction and Manning’s n parameter learning across extensive areas. We employ the δHBV-HydroDL model, trained on the 3000 GAGES-II dataset, for streamflow predictions across CONUS HUC10 basins. These predictions are then integrated with MERIT basin data and processed through our differentiable routing model, which learns reach-scale parameters like Manning’s n and spatial channel coefficient q via an embedded neural network. This approach enhances national-scale flood simulations by leveraging a learned Manning’s n parameterization, directly contributing to the refinement of CONUS-scale flood modeling. Furthermore, this method shows promise for global application, contingent upon the availability of streamflow predictions and MERIT basin data. Our methodology represents a significant step forward in the spatial scaling of differentiable river routing models, paving the way for more accurate and extensive flood simulation capabilities.