Multiple Models to Inform Decision Making

Environmental simulation models are invaluable tools for informing decision making. For example, we depend on hydrologic models for water supply/flooding decisions, water quality models for Total Maximum Daily Loads (TMDLs), and air quality models for standards compliance assessment. When multiple models are available, it is not uncommon to apply more than a single model to inform a decision, particularly if the consequences of a decision are significant, and uncertainties are believed to be large.

Consider weather forecasting for major storms such as hurricanes. It is now common practice for a meteorologist to display the projected deterministic trajectories of the hurricane’s eye based on several model forecasts; the fact that these models are based on different mathematical constructs adds to the robustness of the envelope covering the range of storm trajectories. It also provides greater public awareness of possible outcomes than does a single deterministic model trajectory.

In a presentation in the UMCES IAN Seminar Series for the Chesapeake Bay Program (http://ian.umces.edu/seminarseries/video/72/), I urged that the Bay Program develop a second water quality model that would provide important information on the uncertainty in the assessment of the impact of proposed management actions, information that the current Chesapeake Bay Model (CBM) cannot provide. The CBM was developed and continues to be refined by an experienced, excellent modeling team. Yet, as a single deterministic model, the CBM shares a critical shortcoming with the most sophisticated meteorological model; both are deterministic simplifications of an extremely complex system. We have seen that even the most elaborate CBM is prone to public criticism and skepticism concerning the dependency of decisions on the CBM forecasts, particularly in the absence of uncertainty analysis or an envelope covering the range of possible outcomes associated with management options.

Multiple models have been quite effective in the past for informing water quality management decisions. For example, the Great Lakes agreement in the early 1980s was aided by multiple models, and more recently, I was involved in a successful multiple models assessment for the Neuse River Estuary (NC) nitrogen TMDL. For the Neuse Estuary, three models were developed and applied: (1) a laterally-averaged version of CE-QUAL-W2, (2) a three-dimensional version of EFDC-WASP, and (3) a probabilistic Bayes network model (see Craig A. Stow, Chris Roessler, Mark E. Borsuk, James D. Bowen, and Kenneth H. Reckhow. 2003. A Comparison of Estuarine Water Quality Models for TMDL development in the Neuse River Estuary. Journal Water Resources Planning and Management. 129:307-314 for a comparison of the model applications). The first two models applied in the Neuse study provided substantial spatial/temporal/ecological detail, while the third model provided less detail but could be used to estimate uncertainties of model forecasts.  The Neuse case study demonstrated the value of multiple models in enriching the overall assessment of the impact of management actions. Even the best model usually cannot deal with all issues that are important to stakeholders and decision makers. Developing and applying a second model that helps to address different issues that are difficult for the first model to address should add little additional cost while adding substantial useful information.