A fast high resolution distributed hydrological model for forecasting, climate scenarios and digital twin applications using wflow_sbm

We investigated improvements to further speed up the multi-threaded scaling of the distributed hydrological model wflow_sbm. To gain insight in the speed improvements for operational applications, we connected the improved code to ECMWF's Fields Database to allow for on-the-fly pre-processing of the forcing, which accelerated the entire forecasting chain. In the original wflow_sbm implementation, run times increased when more than eight threads were used due to Julia's native threading overhead. Now, run times are 2 to 11 times faster, depending on the chosen routing scheme, number of threads and catchment size. We show the advantages of the improvements in a test setup where ECMWF forecasts and 35 years of ERA5 reanalysis data were used to force wflow_sbm models at 1x1 km spatial resolution for Europe. The attained speedup allows for using distributed hydrological models in large-scale hydrological forecasting and climate-change applications, which is currently often limited to lumped models.