Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy

The study addresses the challenge of integrating complex landscape-hydrological interactions into predictive models for improved water resource management. The aim is to investigate the effectiveness of landscape metrics—quantitative indices measuring landscape composition and configuration—as predictors of WES in the Arno River Basin, Italy. Utilizing two hydrological models alongside a random forest algorithm, we assessed spatial and temporal variations in water yield, runoff, and groundwater recharge. The findings indicate that landscape metrics derived from high-resolution land use data significantly impact WES outcomes. Specifically, the models demonstrated average landscape metric importances of 16.8 % for spatial and 17.8 % for temporal predictions concerning runoff. For water yield, these averages were 32.9 % spatially and 43.5 % temporally, while groundwater modeling showed importances of 14.09 % spatially and 33.8 % temporally. Key landscape metrics identified include the core area index for broad-leaved forests and the perimeter-to-area ratio for non-irrigated agricultural areas as critical spatial and temporal predictors of water yield and groundwater recharge. Thresholds were observed, indicating landscape configurations that minimize hydrological variability. For instance, runoff variation is minimal when the landscape exhibits high forest fragmentation (over 1000 coniferous patches), low aggregation (aggregation index <75), and reduced connectivity (cohesion index under 80). Similarly, groundwater variation is minimized with decreased boundary length of vegetation patches (perimeter-to-area ratio <0.8), agricultural lands (perimeter-to-area ratio under 1), and the presence of low core agricultural areas (core area index above 8). The identified thresholds could inform land-use policies, such as targeted afforestation or crop diversification strategies, to optimize WES provision.