What can hydrological modelling gain from spatially explicit parameterization and multi-gauge calibration?

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, enhancing the spatial representation of models while imposing stronger calibration constraints offers a promising pathway to reinvigorate the predictive capabilities of physically based distributed hydrological models. However, beyond their effects on aggregated simulated responses, 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. The framework is applied a representative nested catchment through a series of intensive comparative calibration experiments, in which multiscale parameter regionalization technique is integrated with the Variable Infiltration Capacity model.

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 (with %BiasFLV worsening by 8.6 %). On the other hand, multi-gauge calibration markedly enhances parameter identifiability by imposing stronger constraints on spatially shared parameters. This creates a trade-off with spatially explicit parameterization, which expands the parameter set, thereby reducing identifiability and subsequently increasing equifinality. Take them together, a cross-benefit can be clearly identified in the multidimensional objective space during calibration. We found that, under uniform parameterization, continuous and convex arc-shaped Pareto fronts emerged, reflecting pronounced competition among multi-gauge objectives. This competition is substantially alleviated under spatially explicit 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.