Large datasets and complex processing requirements can lead to substantial time costs when developing and initializing hydrologic models. Our research builds upon the groundwork of Knoben et al. (2022) to improve a methodology that streamlines the time investments required for hydrologic model configuration. Our strategy introduces a compartmentalized framework that divides the configuration workflow into model-agnostic and model-specific segments. Within the model-agnostic component, we introduce a suite of modules that perform specific data processing tasks. Examples are adapting large meteorological datasets to any desired temporal or spatial region, and standardized Geographical Information System (GIS) operations that are essential in the configuration phase of hydrological modelling. These operations predominantly involve narrowing down extensive domain data to a specified scope and the conversion of gridded data into model elements. These workflows are fine-tuned for deployment on high-performance computing architectures, thus ensuring efficient and thorough processing capabilities for large-domain hydrological studies. This structure provides an accessible, open-source, and adaptable workflow that aligns with the requirements of hydrological modelling teams. These workflows are complemented by the HYDRANT (Hydrological Analysis Toolbox) Python package that provides all the extra tools necessary in hydrological modelling and analysis. Finally, model-specific workflows are customizable for varied research and operational environments, including but not limited to MESH, SUMMA, and HYPE models. The dissemination of these workflows to the community at large is intended to enhance the efficiency of hydrological modeling endeavors and shift the focus to scientific analysis of modelling results instead.