Qualitative Uncertainty Assessment for Distributed Surface Water Quality Models

Water quality models are often used to aid water quality regulators in the United States and around the world make critical decisions about how to improve water quality. Basin scale water quality models generally simplify spatial complexity to some degree. This problem is especially relevant in water quality modeling since the sources of pollution as well as the hydrologic drivers vary spatially across the landscape. The spatial complexity problem also presents specific challenges for estimating model predictive uncertainty. Questions of how to integrate multiple sources of stream water and water quality data at multiple locations are likely to be even more daunting than they are for surface water hydrologic models. At this time, given the complexity of water quality models and the general lack of data available, true statistical techniques of integrating, multiple data sources and calculating uncertainty bounds are probably not reasonable approaches. For this reason, we propose evaluation methods that are not based on statistics in order to evaluate the fit-to- purpose of particular model applications. Within SWAT2005, both a statistical method for uncertainty analysis, ParaSol, and an evaluation method, SUNGLASSES, have been incorporated. Additionally weighted averages of prediction errors are integrated into a global objective function and used to evaluate the reasonableness of model assumptions. This integration occurs across multiple objective functions and multiple locations. These methods have been applied in the context of a hypothetical water quality control problem in the San Jacinto watershed in southern California. The analysis includes an extension of the uncertainty analysis into its economic implications. The economic impact of the uncertainty methods proposed here are compared to more traditional uncertainty methods. The economic implications of uncertainty are shown to be important but only under circumstances where water quality targets are close to being met.