The description of the locations of uncertainty will vary according to the assessment method (model) that is in use. Nonetheless, it is possible to identify certain categories of locations that apply to most models.

• Context refers to the choice of the boundaries of the system to be modeled. This location is of great importance, as the choice of the boundaries of the system determines what part of the real world is considered inside the system (and therefore the model), and what part of the real world is left out. The choice of the system boundaries is often referred to as the 'problem framing', 'problem definition' or “issue framing”.  Different stakeholders have different perceptions of what constitutes a risk, which risks should be assessed, and how much risk is acceptable, so contextual uncertainties may be a cause for considerable debate during (and after) an assessment. 
• Model structure refers to the variables, parameters and relationships that are used to describe (model) a given phenomenon.  Model structure uncertainty is thus uncertainty about the form of the model that describes the phenomena included within the boundaries of the system.  Here one could think of the shape of dose-response functions, or the additivity vs. the multiplicativity of risk factors.  In situations where the system being studied involves the interaction of several complex phenomena, different groups of analysts may have different interpretations of what the dominant relationships in the system are, and which variables and parameters characterise these relationships. Uncertainty about the structure of the system implies that any one of many model formulations might be a plausible, although partial, representation of the system. Thus, analysts with competing interpretations of the system may be equally right, or equally wrong.
• Inputs refers to the data describing the system. Uncertainty about system data can be generated by insufficient or poor quality data. Measurements can never exactly represent the 'true' value of the property being measured. Uncertainty in data can be due to sampling error, measurement inaccuracies or imprecision, reporting (e.g. transcribing) errors, statistical (e.g. rounding or averaging errors), conflicting data or simply lack of measurements.
• Parameters comprise the specific, quantifiable variables used to describe the system being modelled.  Three main types of parameter can be specified:
  • Exact parameters (e.g. π and e);
  • Fixed parameters, (e.g. the gravitational constant g); and
  • A priori chosen or calibrated parameters.

The uncertainty on exact and fixed parameters can generally be considered as negligible within the analysis.  However, extrapolation of parameter values from a priori experience does lead to parameter uncertainty, as past circumstances are rarely identical to current and future ones. Calibrated parameters are also subject to uncertainty because calibration has to be done using historical data series, and sufficient (i.e. representative) calibration data may not be available, while errors may be present in the data that are available.

• Model outcome (result) is the uncertainty caused by the accumulation of uncertainties from all of the above locations (context, model, inputs, and parameters). These uncertainties are propagated throughout the model and are reflected in the resulting estimates of the outcomes of interest (model result). It is sometimes called prediction error, since it is the discrepancy between the true value of an outcome and that predicted by the model.