Predictive models are always affected by uncertainties. There is not one “right” prediction, rather there is a distribution of possible future results. The recent integration of a stochastic uncertainty analysis module to the Sea Level Affecting Marshes Model (SLAMM) allows users to examine wetland coverage results as distributions and can improve the decision making process. This addition to the SLAMM interface makes it possible to examine the effects of uncertainty and data errors in model parameters, including sea level rise, uplift/subsidence, tide ranges, and accretion and erosion rates, as well as feedbacks between sea level rise and accretion. Uncertainty in the elevation data layer can be assessed while considering issues such as the spatial-autocorellation of measurement errors. Results account for uncertainties in input parameters and driving variables, provide a range of possible outcomes and their likelihood, and allow model users to evaluate the robustness of deterministic results. A stand-alone program, the SLAMM Uncertainty Viewer, was developed with funding from Ducks Unlimited in order to simplify uncertainty output for end users, analysts, and decision makers. The SLAMM Uncertainty Viewer provides a map-based interface that analyzes future wetland-coverage probabilities for a user-defined region. Graphical outputs from the viewer provide quantitative results that can assist in planning and decision-making. Learn more about SLAMM at warrenpinnacle.com/prof/SLAMM.
This webinar is courtesy of the EBM Tools Network.