Enhancing physically based and distributed hydrological model calibration through internal state variable constraints

Hydrological models often struggle to accurately represent subsurface processes, which are crucial for understanding groundwater dynamics and recharge, particularly in snow-dominated catchments. Traditional calibration methods, primarily focused on streamflow, can produce models that perform well for discharge prediction but inadequately capture internal hydrological processes such as groundwater recharge, baseflow, and soil moisture. This study explores how incorporating internal state variables into the calibration process can improve model realism and better reflect the complex interactions within the hydrological cycle.

Using the physically based Water Balance Simulation Model (WaSiM), we implement and compare three model configurations across 34 catchments in southern Quebec, a region characterized by diverse hydrological conditions and significant seasonal snowmelt. The first configuration (Baseline, BL) employs a conventional calibration approach, focusing on streamflow while relying on conceptual methods to simulate groundwater flow. In the second configuration (Groundwater, GW), we enable the groundwater module, which uses physically based equations to model subsurface processes. The third configuration (Groundwater with Recharge Calibration, GW-RC) further refines the model by incorporating groundwater recharge as a constraint in the calibration process.

Our results show that while the BL and GW configurations achieve high Kling-Gupta Efficiency (KGE) scores for streamflow predictions, they underperform in representing other critical hydrological processes, such as groundwater recharge and baseflow variability. The GW-RC configuration, despite a modest reduction in streamflow performance, significantly improves the representation of subsurface processes, particularly during snowmelt periods. This enhancement is achieved by including internal state variables such as recharge in the objective function during calibration. As a result, GW-RC offers a more comprehensive understanding of watershed dynamics and provides insights that are crucial for water resource management and climate adaptation strategies.

The study highlights the value of multi-variable calibration frameworks, which move beyond streamflow optimization to incorporate additional hydrological data. Such frameworks offer a more accurate depiction of watershed processes, especially in the context of climate change. The GW-RC approach demonstrates that even small adjustments in the calibration process, such as the inclusion of recharge as a constraint, can lead to substantial improvements in model realism without sacrificing overall model stability.

The results underscore the importance of developing robust hydrological models capable of simulating both surface and subsurface processes, which are essential for adapting to future hydrological shifts. This study provides a framework for improving hydrological model calibration and offers valuable contributions to the fields of water resource management and climate adaptation.