Coupling SWAT+ and MODFLOW 6 using Python: A flexibleapproach to representing groundwater-surface water interactions in anagricultural catchment

Growing environmental challenges are forcing hydrogeologists to create
increasingly accurate models that faithfully reflect the complex
groundwater-surface water interactions in the hydrological cycle.
However, the construction of complex, monolithic models is often faced
barriers such as lack of detailed input data and time-consuming
calculations. An alternative is to couple dedicated domain models, which
allow existing resources to be leveraged and increase the accuracy of
simulations.

This paper presents a custom approach to coupling the SWAT+ catchment
model (representing surface and soil processes) with the MODFLOW 6
groundwater model. The integration was performed using Python scripts,
which ensured flexibility in data exchange. The study utilizes the SWAT
model developed in QSWAT and a hydrogeological model which was migrated
from the older MODFLOW 96 engine to the latest version of MODFLOW 6 with
using the DIS grid.

The study area is an agricultural catchment of approximately 250 km² in
south-central Poland. The key methodological challenge addressed in this
paper is the issue of spatial scaling, i.e. upscaling and mapping the
irregular hydrological response units (HRUs) from the SWAT model to the
regular computational grid of the MODFLOW model. The algorithm developed
in Python enables bidirectional exchange of fluxes (groundwater
recharge, river flow), which represents the dynamic interaction between
surface water and saturated zone. Preliminary results indicate that the
presented approach improves consistency of the water balance at the
interface.

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).