Quantitative Assessment of Climatic and Reservoir-Induced Effects on River Water Temperature Using Bayesian Network-Based Approach

River flow regulations and thermal regimes have been altered by human-induced interventions (such as dam construction) or climate change air temperature variations). It is of great significance to adopt a well-performed data-driven model accurately quantify the impact over river water (WT), which can help understand underlying evolution mechanism operation change. This research applied Bayesian network-based (BNM), easily identify inherently stronger associated variables with target variable from multiple influencing predict daily WT make quantitative assessment effect produced Three Gorges Reservoir (TGR) construction in Yangtze River, China. A comparative study between proposed two other models was implemented verify predicted accuracy BNM. With BNM model, reservoir impoundment quantitatively analyzed calculating difference reconstructed series observed during post-TGR period. The TGR posed more on variations than induced according results. be concluded follows: October January showed remarkable warming tendency an increase released warmer volumes before, while cooling March June because hysteretic response increasing temperature. shows potential for prediction ecological risk management rivers.