Projecting Future TOC in Data-Scarce Agricultural Reservoirs: A WGAN-Enhanced Framework Revealing the Importance of Dynamic Variability

Climate change poses a significant threat to water quality in agricultural reservoirs. However, projecting future changes using machine learning (ML) is often hampered by the limited availability of long-term observational data. This study develops a robust framework to project future Total Organic Carbon (TOC) concentrations by combining a Wasserstein Generative Adversarial Network (WGAN) for data augmentation with high-performance ML models. We applied this framework to two contrasting South Korean reservoirs (one pristine, one polluted). Historical data (n = 60) for each was augmented using WGAN, and a suite of nine ML models was optimized and evaluated. The best-performing model, M5 Model Tree with Stochastic Gradient Boosting (M5-SGB), was then used to project TOC from 2025–2100 using a 12 Global Climate Model (GCM) ensemble from CMIP6 under SSP2-4.5 and SSP5-8.5 scenarios. Notably, the effectiveness of WGAN augmentation was model-dependent, significantly boosting the performance of certain algorithms while having a negligible effect on others. Future projections revealed a statistically significant increasing TOC trend for both reservoirs. However, the response was site-specific: the pristine but temporally unstable reservoir showed vulnerability even under the moderate-emissions scenario, while the polluted but stable reservoir only exhibited a significant trend under the high-emissions scenario. This study provides a practical framework for water quality prediction in data-limited contexts. The findings demonstrate that a reservoir's vulnerability to climate change is critically linked to its dynamic variability, not just its static water quality grade. This offers crucial insights for designing more effective, risk-based water management and monitoring strategies.