Abstract. Traditional hydrological modelling is facing transformative pressures from the rise of data-driven approaches and increasing demands for modelling realism. With improving data availability, spatially explicit parameterization and multi-gauge calibration offers a promising pathway to enhancing both the predictive capability and the realism of physically based distributed hydrological models. However, current understanding remains largely confined to their broad effects on aggregated simulated responses, while the underlying mechanisms and interactions through which these approaches benefit hydrological modelling remain poorly understood. To bridge this knowledge gap, this study develops an Experiment Framework to evaluate the effect of Spatially explicit Parameterization and Multi-gauge calibration, termed EF-SPM. Implemented through the Variable Infiltration Capacity (VIC) model combined with the multiscale parameter regionalization technique, the framework is applied to the Upper Han River Basin, a representative nested catchment, via intensive comparative calibration experiments. Results indicate that, compared to simpler configurations, considering both spatially explicit parameterization with multi-gauge calibration leads to consistent improvements in streamflow simulations across all sub-basins. Controlled experiments isolating individual effects further show that spatially explicit parameterization is particularly effective in improving simulations under moderate-flow to high-flow conditions (with an 18 % improvement in %BiasFHV1), yet at the cost of degraded performance during low-flow periods. On the other hand, multi-gauge calibration markedly enhances parameter identifiability by imposing stronger constraints on spatially shared parameters. This effectively mitigates information-gap-induced uncertainties, thereby enabling robust parameter transfer to ungauged upstream sub-basins. Importantly, their combined application yields a clear cross-benefit in the multidimensional calibration objective space by substantially alleviating the trade-offs among gauge-specific objectives observed under uniform parameterization. This study integrates two promising directions in contemporary hydrological modelling, highlighting the importance of pursuing more expressive parameterization and stronger calibration constraints in parallel, rather than prioritizing one over the other. In doing so, it provides a steppingstone for advancing distributed hydrological modelling toward a modern Model–Data Infusion framework.
Zheng et al. (Wed,) studied this question.