Bayesian Network Modeling for Risk-Based Water Quality Decisions with Sparse Data: Case Study of the Kiso River

The study aims to explore the causal relationships among climate, hydrological, and water quality variables in the Kiso River Basin, Japan, using a discrete Bayesian Network (BN) model. The BN was developed to represent probabilistic dependencies between climate factors (rainfall, air temperature), hydrological conditions (river flow levels), and water quality indicators (pH, dissolved oxygen [DO], electrical conductivity, ammonia, turbidity, organic pollution, and water temperature). The model used hourly monitoring data collected between 2016 and 2023, and the continuous variables were discretized based on national environmental thresholds to evaluate exceedance probabilities under different hydro-climatic scenarios. Results showed that air temperature strongly influenced water temperature, with a stabilizing effect under constant flow conditions. Rainfall and river flow were key drivers of turbidity; heavy rainfall and high flow increased the probability of exceeding turbidity thresholds by nearly 80%. Elevated ammonia levels during heavy rainfall and low temperatures reflected runoff and limited nitrification processes. Electrical conductivity decreased during high flows due to dilution, while dissolved oxygen was affected by low flows, turbidity, and temperature. As static BNs cannot model temporal dynamics, supplementary cross-correlation analyses were conducted to assess short-term responses among variables, revealing that most water quality parameters respond within ±24 h to changes in hydrological conditions. This study demonstrates that discrete BNs can effectively translate long-term monitoring data into practical, decision-relevant risk assessments to support adaptive water quality management in dynamic river systems.