Modular approach to calibration of supra-regional scale integrated surface-groundwater models

This study presents a theoretically sound operational framework for calibrating large-scale, high-fidelity integrated surface water-groundwater models to improve their reliability for water resources management. The approach combines ParFlow-CLM simulations of three-dimensional variably saturated flow with local sensitivity analysis and Gaussian Process Regression surrogates to enable efficient multi-stage calibration against water table depth and river discharge observations. The framework is applied to the entire system associated with the Po River District (87,000 km²) in northern Italy, resulting in the first robustly calibrated high-fidelity model at this spatial scale. Calibrated model parameters include hydraulic conductivities of the main subsurface geomaterials and Manning roughness coefficients of major rivers in the area. Our results show that clay hydraulic conductivity is a primary driver for groundwater table dynamics, while channel roughness dominates river discharge. Overall, the proposed strategy provides a robust computational framework for scenario analysis and sustainable water management under climate and anthropogenic pressures.