<p>Environmental modelling is increasingly constrained by a fundamental dilemma: achieving high spatiotemporal resolution requires dense monitoring, yet accuracy-oriented paradigms continue to treat low-quality data (LQD) as noise to be corrected or discarded, driving up costs and limiting scalability. In this study, we propose and validate a system-level quality–quantity trade-off framework that treats LQD as a conditional information resource instead of a liability. Using Lake Taihu as a representative complex lake system, we demonstrate that biased and noisy data with temporal continuity can provide effective system-level information when appropriately deployed. We further show that spatiotemporal input design, particularly clustered spatial deployment and pulse-like sampling, can substantially amplify system responses, partially compensating for intrinsic data quality limitations. Across all experiments, the relationship between cumulative error input and system response exhibits a robust segmented nonlinear pattern characterized by diminishing marginal returns. Time-decoupled analyses reveal three distinct response regimes, namely efficient assimilation, transitional saturation, and diminishing returns, while repeated-perturbation tests confirm the stability and reproducibility of this structure within the tested system. Building on these findings, we derive the Trade-off Efficiency (TE) curve and identify two operational reference points (AE10 and AE50), which translate abstract trade-off behavior into decision-relevant guidance for LQD utilization. Collectively, the proposed framework integrates prioritized utilization, structural design, and threshold-guided management, marking a shift from controlling uncertainty toward strategically adapting to it in large-scale environmental monitoring.</p>