Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora scaling-related challenges that remain unsolved. To user, light model result interpretation, finer-resolution output might imply an increase understanding complex interplay heterogeneity within system. Here we investigate spatial scaling form varying resolution by evaluating streamflow estimates distributed wflow_sbm based on 454 basins from large-sample CAMELS data set. Model instances are derived at three resolutions, namely 3 km, 1 and 200 m. The show finer does not necessarily lead to better basin outlet. Statistical testing objective function distributions (Kling–Gupta efficiency (KGE) score) resulted only statistical difference between km m estimates. However, assessment sampling uncertainty shows high uncertainties surrounding KGE score throughout domain. makes conclusion inconclusive. do indicate strong locality differences expressed scores average 0.22 with values larger than 0.5. this study open up research paths can changes flux state partitioning due scaling. will help further understand need be resolved for