Uncertainty Assessment in Watershed-Scale Water Quality Modeling and Management

Watershed-scale water quality models such as SWAT, WARMF or HSPF are widely used to support management decision-making. However, the uncertainty in model output is hard to determine explicitly. An uncertainty analysis is necessary to develop a safety factor, or in regulatory terms a Margin of Safety (MOS) for the Total Maximum Daily Load (TMDL). We have developed a framework to systematically assess the uncertainty in complex models, with a particular emphasis on watershed models used for decision-support. A key component of the framework is the Management Objectives Constrained Analysis of Uncertainty (MOCAU) method, which explicitly considers management objectives and observational uncertainty within the analysis. In this case study, we used the WARMF model and a specific catchment in an actual watershed (Santa Clara River) to demonstrate the applicability of the approach. A series of numerical experiments were conducted to investigate the performance of MOCAU. Although this method relies on a Monte Carlo approach, the use of management criteria, such as Non Attainment Frequency, Severity of Exceedance, Timing of Exceedance, are used to better constrain the uncertainty analysis. In addition to determining the necessary information for the MOS, a key result is the ability to design monitoring programs that use resources much more efficiently, by directing them towards those conditions that are most likely to reduce the uncertainty of management/regulatory decisions.