Modeling in Environmental Assessments

Models provide a structured means of evaluating complex environmental systems, exploring potential outcomes, and supporting environmental assessments where direct empirical observations may be limited. Because models are simplified representations of natural systems, their predictions depend on the quality and quantity of available data, the appropriateness of the modeling approach, the assumptions on which they are based, and the sources and levels of uncertainty associated with each. The credibility of any model depends not only on its sophistication, but also on the quality of its underlying data, the transparency of its assumptions, and its consistency with empirical observations. Model outputs should therefore be interpreted within a broader weight-of-evidence framework that also considers empirical observations, monitoring results, operational experience, and other relevant scientific information. 

EnerGeo supports modeling approaches that are transparent, reproducible, and scientifically robust. Model assumptions should reflect the best available scientific evidence, and uncertainty should be clearly characterized with relevant assumptions clearly stated. When multiple conservative assumptions are incorporated throughout a modeling framework, they may compound uncertainty and substantially influence predicted exposure, risk, or environmental effects, potentially resulting in estimates that differ significantly from real-world conditions. 

Environmental models are most valuable when they are periodically evaluated and refined as new scientific information becomes available. Integrating predictive modeling with empirical observations improves confidence in model performance, reduces uncertainty, and supports more scientifically informed environmental assessments.