Evaluating the Effects of Parameter Uncertainty on River Water Quality Predictions

Abstract

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors’ findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources.

Keywords

• water resources
• uncertainty analysis
• sensitivity analysis
• river water quality modeling
• HSPF