Towards water resilience: A multi-stage calibration framework for large-scale integrated surface–subsurface hydrological models

This study introduces a methodological framework for calibrating large-scale, high-fidelity integrated surface water–groundwater models with the aim of enhancing their reliability for water resource management. Our approach synergistically integrates ParFlow-CLM for simulating three-dimensional variably saturated flow, local sensitivity analysis to identify relevant model parameters, and Gaussian Process Regression surrogates for efficient multi-stage calibration against water table depth and river discharge observations. The framework is then exemplified through the analysis of the groundwater flow scenario associated with the Po River District (87,000 km2) in northern Italy. The workflow yields the first robustly calibrated high-fidelity model at such spatial scale, embedding calibrated values of (i) hydraulic conductivities of the main geomaterials forming the internal architecture of the subsurface and (ii) Manning roughness coefficients of the major rivers in the domain. Our results highlight conductivity of clay as a dominant parameter driving groundwater table dynamics while channel roughness is the most important parameter for river flows. Our large-scale model calibration strategy offers a robust conceptual and computational environment for scenario analysis and sustainable water planning in the presence of climate- and anthropogenic-related pressures.