Uncertainty analysis in a large-scale water quality integrated catchment modelling study

Area of Science:

• Environmental Science
• Hydrology
• Water Quality Modeling

Background:

• Integrated catchment models are essential for urban water quality simulation but often yield uncertain results.
• Uncertainty analysis is underutilized in practice, with limited understanding of individual model element contributions.
• Computational and organizational constraints often restrict these analyses to smaller systems.

Purpose of the Study:

• To present an uncertainty propagation and decomposition scheme for integrated water quality modeling.
• To evaluate dissolved oxygen dynamics in a large-scale urbanized river catchment in the Netherlands.
• To identify dominant uncertainty sources in urban river water quality predictions.

Main Methods:

• Utilized an uncertainty propagation and decomposition scheme for integrated water quality modeling.
• Applied forward propagation of measured and elicited uncertainty input-parametric distributions.
• Contrasted model predictions with monitoring data series for calibration and validation.

Main Results:

• Initial uncertainty in water quality-quantity parameters led to significant dissolved oxygen prediction uncertainty, highlighting the need for calibration.
• Post-calibration, combined sewer overflow pollution loads and rainfall variability emerged as dominant uncertainty sources.
• Model insights aid in directing future monitoring and modeling efforts for urban river systems.

Conclusions:

• Formal calibration is essential to adapt water quality models to local river dynamics.
• Combined sewer overflows and rainfall variability are critical uncertainty drivers in urban river dissolved oxygen modeling.
• Existing national guidelines for mitigation selection may need adaptation to account for model uncertainties.