Assessing sediment connectivity with the InVEST model and a structural approach: a case study

Abstract

Sediment connectivity is a fundamental concept for understanding sediment transport processes and managing watershed health. This study addresses the existing gap in comprehensive comparisons of sediment connectivity indices by evaluating two widely used methods: the structural-based Borselli index (IC_B) and the process-based InVEST-SDR model (IC_InVEST). Both models were applied to the Taleghan watershed in Iran, utilizing a digital elevation model (DEM) combined with spatial datasets, including land cover, soil type, and rainfall erosivity. The evaluation employed statistical tools such as correlation analysis, Wilcoxon signed-rank test, linear regression, and root mean square error (RMSE) to quantify model performance against field-based sediment connectivity (FIC) data. Results indicate that IC_InVEST achieved a higher correlation (r = 0.88) with FIC and explained 77 % of sediment variation, outperforming IC_B which explained 62 %. However, limitations in field data necessitate cautious interpretation. This research highlights the importance of integrating both topographic and process-based factors in sediment connectivity assessment and underscores the need for standardized methodologies and extensive field validation. Furthermore, a regression-based adjustment was proposed to enhance comparability between indices. The findings suggest that model selection should be guided by data availability, watershed characteristics, and specific management objectives. InVEST-SDR’s relatively low data requirements make it practical for identifying erosion-prone hotspots, facilitating proactive watershed protection. Future research should focus on refining sediment connectivity models and expanding validation efforts to improve their reliability across diverse environments.