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

Study region: Upper region of the Greater Chao Phraya River (GCPR) basin in Thailand. Study focus: The upper GCPR basin is highly regulated by multipurpose reservoirs, which obviously have altered the natural streamflow. Understanding quantitative effects of such alteration is crucial for effective water resource management. Therefore, this study aims to assess how reservoir operation affects the water balance, daily flow regime and extreme flows in this basin. For this purpose, we reconstructed streamflow in the naturalized (no reservoir) and baseline operation scenarios using the (∼1 km resolution) distributed model. To overcome data scarcity, we ran the model with global data and parameterization. A target storage-and-release-based reservoir operation module was applied in the baseline operation scenario. The model results were analyzed in comparison to observations in a wet year, a dry year, and the period 1989–2014. New hydrological insights for the region: The reservoir operation resulted in more evaporation. It inverted the natural flow seasonality and smoothed the daily flow regime with decreasing high flows, increasing mean flows and low flows, greater baseflow contribution, and lower flashiness. It prevented or mitigated many historical extreme flow incidents. The annual flood peaks and minimum flows were markedly mitigated in terms of both magnitudes and frequencies, but their timing became more variable and difficult to predict. Altogether, the results highlighted the importance of effective decision making for real-time operation, which remain challenging in practice.