From Land to Water: The Impact of Landscape on Water Quality Through Linear Models

This work explores the relationship between landscape metrics and surface water in the Galicia-North Portugal Euroregion, employing 6,220,767 linear regression models through Python scripts to predict surface water quality. The Iberian Biological Monitoring Working Party (IBMWP) index, based on benthic macroinvertebrate communities from 40 sites across Portugal (PT) and Galicia (GL), served as the biological indicator. The models were initially selected based on linear regression assumptions (17 tests), and validated against real-world data, evaluating statistical performance through indicators such as R-squared, mean absolute percentage error (MAPE), and percentage bias (PBIAS). Results indicated that GL had a higher macroinvertebrate abundance, whereas Portugal showed greater diversity and family richness. Statistical analysis revealed that landscape significantly influenced water quality, with land use composition and configuration driving differences in ecological conditions between regions. The best-performing models demonstrated a high R-squared value of 0.7, a MAPE of 27% for calibration (PT) and 10% for the validation (GL), indicating a strong predictive relationship. The models provide valuable insights into the complex interactions between landscape patterns and water quality, highlighting how variations in landscape structure can directly affect ecological integrity. These findings reinforce the need for strategic land management to preserve water quality and emphasizing the importance of transboundary governance across the Euroregion to foster sustainable development.