Parameter Sensitivity and Limits to Ecological Prediction in a Coupled GOTM–FABM–WET Application to a Shallow Lake in China

sWe evaluated the diagnostic and predictive performance of the coupled General Ocean Turbulence Model–Framework for Aquatic Biogeochemical Models–Water Ecosystems Tool (GOTM–FABM–WET) in shallow Kun Cheng Lake, China. Monthly near-surface observations supported model calibration during 2021–2023 and out-of-sample ecological validation during 2024–2025. Model skill was assessed through one-at-a-time parameter screening, moment-independent Delta sensitivity analysis with sample-size convergence diagnostics, sequential multi-parameter calibration, and comparisons against statistical and nutrient mass-balance benchmarks. Water temperature calibration was strong (Nash–Sutcliffe efficiency, NSE = 0.933), although temperature observations were available only for 2021–2023. Dissolved oxygen (DO) exhibited modest validation skill (NSE = 0.290). Nitrogen parameter refinement improved correlation direction and simulated variability, yet the simulated seasonal TN peak remained five months earlier than observed. Total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chl-a) all retained negative validation NSE values. In particular, Chl-a calibration remained inadequate (calibration NSE = −0.405; validation NSE = −0.481), and its dedicated calibration stage retained the incoming seed parameters. Only 0.50–0.78% of evaluated candidates satisfied the cross-variable calibration guardrails. Temporally constant nutrient boundaries, coarse TP reporting resolution, and frequent numerical state repairs highlight key priorities for model improvement. Overall, the current evidence supports retrospective near-surface thermal diagnosis and conditional interpretation of broad seasonal DO patterns, whereas reliable nutrient and algal prediction will require time-varying inflow and nutrient chemistry, process-level field observations, and independent verification of seasonal timing and spatial representativeness.