An Integrated Computational Framework for the Advanced Design of Vegetated Filter Strips in Agricultural Landscapes

Effective management of agricultural runoff is essential to safeguard water quality and promote sustainable land use. Vegetative filter strips (VFS), areas of dense vegetation established between pollution sources and receiving surface waters, are widely implemented as a nature-based solution to mitigate nutrient and sediment export from agricultural fields. Their performance is commonly assessed using established computer models such as VFSMOD. While VFSMOD provides a robust, physically-based representation of VFS performance, its conventional application is largely deterministic, limiting its ability to address the intrinsic environmental variability and uncertainty on VFS design for environmental management.

This work presents a new computational tool consisting of a graphical user interface built upon VFSMOD, specifically developed to enhance the design and assessment of vegetative filter strips under variable conditions. The cross-platform tool extends VFSMOD by incorporating a hypothesis-testing framework for model calibration based on observational data. Once calibration is achieved, the tool supports an advanced VFS design phase in which input uncertainty is considered and mitigation performance in terms of both efficiency and reliability.

The methodology was applied to a real-world agricultural setting. The case study demonstrates how the proposed tool facilitates robust VFS design while explicitly accounting for input uncertainty. Results indicate improved decision support compared with the previous user interface used to run VFSMOD.