Enhancing data preparation for hydrological modeling: a Python-based approach for coupling SWAT+ and MODFLOW

This research focused on the application of Python-based tools for efficient preparation and processing of input data for hydrological modelling in an agricultural catchment of the Kocinka River (SW Poland). Prepared scripts and workflows address the challenge of integration of many data sources required for SWAT+ and MODFLOW models. The presented study focuses on automation of data preprocessing tasks and model calibration support, with option to reuse scripts in future work with similar data for different areas.

The Python-based approach utilizes various libraries, like: GeoPandas for processing spatial data from vector maps, Pandas and Numpy for handling meteorological time series from the Polish Institute of Meteorology and Water Management (IMGW), and Flopy for MODFLOW data management. The scripts streamline the preparation of weather and soil input data specifically formatted for SWAT+ Editor and QSWAT, significantly reducing manual data handling and potential errors in the data preparation phase. The automated workflow particularly benefits the processing of data from agricultural areas, which comprise 66% of the catchment area, ensuring consistent handling of land use parameters across the modeling domain.

The data processing framework incorporates multiple data inputs: meteorological data including precipitation, temperature, and other climate variables, detailed soil maps and land use information as well as satellite data about solar radiation (SARAH-2). The system processes river stage data from three profiles with 30-minute temporal resolution, complemented by flow measurements for hydrological validation. 

The developed Python tools also support the model calibration process by enabling rapid modification of input parameters and automated analysis of water balance components. This approach allows for efficient sensitivity analysis and model refinement, particularly beneficial for understanding the groundwater-surface water interactions.

The study contributes to good modeling practices by providing examples of efficient data preprocessing workflows and calibration support tools, essential for complex hydrological studies that combine multiple data sources and modeling platforms. The automated approach not only saves time but also enhances reproducibility and transparency in the modeling process. 

Acknowledgements. The work was carried out as part of WATERLINE project (2020/02/Y/ST10/00065), under the CHISTERA IV programme of the EU Horizon 2020 (grant no. 857925) funded by National Science Centre, Poland and a partially by AGH University of Krakow, Faculty of Geology, Geophysics and Environmental Protection (grant no. 16.16.140.315).