HR: 14:30h
AN: H53I-04 INVITED    [Abstracts]
TI: Uncertainty Assessment for Surface Water Quality Models: The Challenge of Sparse Data
AU: * Meixner, T
EM: tmeixner@hwr.arizona.edu
AF: University of Arizona, Department of Hydrology and Water Resources, Tucson, AZ 85721, United States
AU: Bubb, K
EM: Kristi.Bubb@erg.com
AF: Eastern Research Group, Inc., 14555 Avion Parkway, Suite 200, Chantilly, VA 20151-1102, United States
AU: van Griensven, A
EM: A.vanGriensven@unesco-ihe.org
AF: UNESCO-IHE Institute for Water Education, P.O. Box 3015, delft, 2601 DA, Netherlands
AB: Water quality models are often used to aid stakeholders in making critical decisions about how to improve water quality. These decisions occur against a backdrop of process complexity and data scarcity. 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 sparse data availability, true statistical techniques of integrating, multiple data sources and calculating uncertainty bounds are not reasonable approaches. For this reason, we have proposed uncertainty estimation methods not based on statistics but instead ones that fall in a class of methods that could be called "fit-to-purpose" methods. Within SWAT2005, both a statistical method for uncertainty analysis, ParaSol, and an evaluation method, SUNGLASSES, have been incorporated. The focus of calibration lies on capturing times series variability whereas the evaluation and uncertainty assessment of the model relies on predictions of sediment mass flux- the purpose motivating model development. These methods have been applied in the context of a water quality 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 predictive uncertainty was compared to traditional margin of safety approaches. The economic implications of improved uncertainty assessment were shown to be most important under circumstances where water quality targets were close to being met.
UR: http://hwr.arizona.edu/tmeixner
DE: 1800 HYDROLOGY
DE: 1862 Sediment transport (4558)
DE: 1871 Surface water quality
SC: Hydrology [H]
MN: 2007 Fall Meeting