Integrated modelling under uncertainty in watershed-level assessment and management

Area of Science:

• Environmental Science
• Water Resource Management
• Environmental Modeling

Background:

• Water quality model predictions face significant uncertainty from natural variability, model structure, and parameter estimation.
• Integrated modeling systems are crucial for predicting receiving water quality and informing watershed management strategies.

Purpose of the Study:

• To describe and demonstrate an integrated modeling system (modified-BASINS) for water quality prediction under uncertainty.
• To evaluate the impact of different uncertainty types on model outputs.
• To assess the effectiveness of watershed management practices on reservoir water quality.

Main Methods:

• Utilized a modified-BASINS integrated modeling system.
• Employed Monte Carlo simulation to investigate uncertainty effects.
• Evaluated scenarios including wastewater treatment plants (WWTP) and constructed wetlands (WETLAND).

Main Results:

• Without management practices, 2012 Hwaong Reservoir T-N and T-P concentrations were predicted below 4.4 and 0.23 mg L(-1) (90% confidence), respectively.
• The combined WWTP + WETLAND scenario showed the greatest improvement, reducing T-N and T-P to below 3.4 and 0.14 mg L(-1) (90% confidence).
• This combined approach yielded 24% T-N and 41% T-P reductions.

Conclusions:

• Monte Carlo simulation within the integrated system is practical for quantifying uncertainty and ensuring reliable water quality predictions.
• The approach enables risk-based decision-making for watershed management.
• Application of this method is recommended for probabilistic water quality assessments.