Chapter 2 - Model Input Data Uncertainty and Its Potential Impact on Soil Properties

Abstract

In order to achieve a highest quality in modeling, detailed information (especially spatially continuous data) about the environment where they are applied is needed. When using measurement data in a model, scientists start from the assumption that this data represents as an unquestionable “truth”. However, as this chapter will illustrate, that the methods used for collecting data are not without uncertainty, and it is crucial to take these uncertainties into consideration and communicate them appropriately, because this uncertainty can translate and propagate into considerable uncertainties in the model output. We demonstrate on two case studies applying remote sensing and geophysical technologies by looking at modeling, uncertainties, and how these uncertainties can be assessed during the modeling process. The case studies reveal that input data uncertainties might cause negligible to large uncertainties in model output depending on study site conditions, applied environmental model, and initial model boundary conditions.