A Deterministic Calibration Strategy for MOHID-Land Based on Soil Parameter Uncertainty

This study investigates the influence of parametric uncertainty in the van Genuchten–Mualem (VGM) model on hydrological simulations and proposes a deterministic, soil-focused calibration strategy within the MOHID-Land model. The approach was applied to the Pedro do Rio watershed to quantify the impact of VGM parameters, typically estimated via pedotransfer functions, on streamflow performance and to reduce uncertainty through targeted calibration. A one-at-a-time sensitivity analysis using the 95% Prediction Uncertainty (95PPU) metric identified the saturated water content (θs) and pore-size distribution (n) as the most influential parameters. Calibration scenarios adjusting these parameters, especially Scenario S45 (+30% θs, +20% n), significantly improved model performance, increasing the Nash–Sutcliffe Efficiency (NSE) from 0.20 to 0.66 on a daily scale and to 0.80 on a monthly scale during the validation period. Subsequent hydrodynamic refinements raised the daily NSE to 0.72, while monthly performance remained unchanged. The results underscore that soil parameter uncertainty plays a central role in long-term water balance representation, while hydrodynamic parameters primarily influence short-term dynamics in steep, responsive basins. Overall, the proposed strategy provides a computationally efficient alternative to fully automatic calibration methods, delivering robust performance while maintaining physical consistency, particularly in data-scarce environments.