Quantifying Cumulative Turbidity Stress on Riverine Biota Using Sensitivity-Based Indices and Generalized Additive Models

Turbidity and fine sediment are widely recognized as key stressors in river ecosystems; however, most ecological assessments rely on short-term concentration metrics that fail to capture the cumulative and time-lagged nature of turbidity impacts. This study proposes an integrated framework to quantify and predict biological responses to cumulative turbidity exposure using sensitivity-based indices and generalized additive models (GAMs).

Long-term suspended sediment (SS) exposure was quantified as cumulative dose (time-integrated SS concentration, mg·hour/L) over multiple antecedent periods (1, 3, and 6 months), including threshold-exceedance metrics describing the duration and magnitude of exceedance above ecologically relevant SS levels. Based on species-specific correlations with cumulative turbidity exposure, a novel Turbidity Sensitivity Index for benthic macroinvertebrates (TSI-BM) was developed by weighting taxa according to sensitivity classes derived from monotonic response patterns of Ephemeroptera, Plecoptera, and Trichoptera (EPT) assemblages. The index was applied to 22 monitoring sites in the upper North Han River Basin, Korea.

Results showed that TSI-BM exhibited a strong and consistent negative correlation with six-month cumulative turbidity exposure (Spearman’s ρ = −0.46, p < 0.001), outperforming conventional indices. GAM-based models further revealed nonlinear main effects and interactions among cumulative turbidity, hydraulic conditions (water depth and velocity), and nutrient concentrations on benthic community sensitivity, with site-wise cross-validated coefficients of determination (R²) ranging from 0.40 to 0.72.

Overall, this study demonstrates that cumulative turbidity exposure, combined with sensitivity-weighted biological indices and flexible nonlinear modeling, provides a robust approach for diagnosing and predicting ecological degradation under sediment stress. The proposed framework offers a transferable tool for river monitoring, impact assessment, and adaptive sediment management under increasing hydrologic variability.

This work was supported by the Korea Environmental Industry and Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment (MOE) (RS-2021-KE001374).