Hydrological modelling is a valuable tool to support sustainable and resilient water management, particularly as we adapt to climate change. The calibration of hydrological models, however, can be a difficult and daunting task. This process varies significantly depending on the modeller, available data, model objectives, calibration parameters, and the optimization algorithms used, among others. This results in a wide range of model parameter sets that often lack reproducibility and consistency._x000D_ _x000D_ This research proposes an agnostic strategy to create workflows to calibrate hydrological models, with the aim to generalize the calibration process to create nearly replicable calibrated models. This strategy can be used to calibrate lumped, semi-distributed, or fully distributed models. The framework is tested using the MESH (Modélisation Environnementale communautaire - Surface Hydrology) model to calibrate a vector-based model for the province of Alberta. The results are compared by analyzing the sensitivity of the calibration to different variables within the strategy, showing the potential of this approach to enhance reproducibility and model practices compared to traditional strategies.
