Purpose: With the growing demand for ecological restoration and accompanying use of ecological models to inform decision-making, there is a pressing need for efficient and effective model evaluation procedures in restoration planning. Ecological complexity and diverse modeling frameworks (e.g., quantitative habitat models versus semiquantitative assessments versus qualitative professional judgment) each create challenges for the verification and validation of ecological forecasting tools. This study evaluates the effectiveness of a semiquantitative riparian assessment model, the Simple Model for Urban Riparian Function (SMURF), relative to herbaceous vegetation community data collected from Beargrass Creek in Louisville, Kentucky. Vegetation community metrics were collected at 15 sites, including species richness, diversity, and native versus invasive species composition. These metrics were compared to multiple SMURF outputs to evaluate model accuracy and reliability. Findings indicate that SMURF metrics are minimally predictive of the empirical vegetation metrics examined here (i.e., 5 of 32 models met criteria for statistical significance). However, vegetation-oriented components of the SMURF model more directly aligned with empirical observations, potentially indicating that the aggregation of many metrics in SMURF makes comparisons with empirical data inappropriate. This study demonstrates the challenges in evaluation of broad-scoped, multimetric indices like SMURF relative to empirical metrics for a particular taxonomic group. These findings also provide insight into the potential limits of commonly used semiquantitative methods for ecosystem assessment.