A Data-Driven Framework for Characterizing Nonlinear Responses of River Ice Growth and Decay in the Shisifenzi Reach of the Upper Yellow River

The Shisifenzi Reach, with its sharp curvature, serves as a prime observational target for river ice research in the Upper Yellow River. Using six winter seasons (2020–2026) of monitoring data, this study developed a Random Forest with SHAP and Accumulated Local Effects (ALE) to characterize nonlinear responses river ice growth and decay to 26 hydro-thermal features. Distinctive contributions are: (1) The 30-day cumulative freezing degree-hours is the dominant driver, and a threshold near 4000 °C·h separates slow growth from rapid thickening. (2) Water level elevation, though indirect, shows an obvious nonlinear link: ice is thickest when it stays in the 988.5–989 m range; above this, the ALE effect drops—often because flow velocity disturbance is slight in the range of 988.5–989 m and high water level mainly coincides with warmer temperature. (3) Short-term thermal features show little effects. Other thermal variables and feature interactions also show saturation. (4) Combining the ALE marginal effect thresholds with observed freeze-up data clarifies the two-stage nonlinear mechanism. In the final decay stage, ice disappears faster than temperature variations alone explain. The data-driven framework provides interpretable, site-specific information useful for river ice management.